Overview

Dataset statistics

Number of variables70
Number of observations19595
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.6 MiB
Average record size in memory568.0 B

Variable types

Numeric25
Text21
DateTime2
Categorical8
Unsupported12
Boolean2

Alerts

latitude is highly overall correlated with longitudeHigh correlation
longitude is highly overall correlated with latitudeHigh correlation
accommodates is highly overall correlated with priceHigh correlation
price is highly overall correlated with accommodatesHigh correlation
minimum_nights is highly overall correlated with minimum_minimum_nights and 2 other fieldsHigh correlation
maximum_nights is highly overall correlated with minimum_maximum_nights and 2 other fieldsHigh correlation
minimum_minimum_nights is highly overall correlated with minimum_nights and 2 other fieldsHigh correlation
maximum_minimum_nights is highly overall correlated with minimum_nights and 2 other fieldsHigh correlation
minimum_maximum_nights is highly overall correlated with maximum_nights and 2 other fieldsHigh correlation
maximum_maximum_nights is highly overall correlated with maximum_nights and 2 other fieldsHigh correlation
minimum_nights_avg_ntm is highly overall correlated with minimum_nights and 2 other fieldsHigh correlation
maximum_nights_avg_ntm is highly overall correlated with maximum_nights and 2 other fieldsHigh correlation
availability_30 is highly overall correlated with availability_60 and 3 other fieldsHigh correlation
availability_60 is highly overall correlated with availability_30 and 3 other fieldsHigh correlation
availability_90 is highly overall correlated with availability_30 and 3 other fieldsHigh correlation
availability_365 is highly overall correlated with availability_30 and 3 other fieldsHigh correlation
number_of_reviews is highly overall correlated with number_of_reviews_ltm and 1 other fieldsHigh correlation
number_of_reviews_ltm is highly overall correlated with number_of_reviews and 1 other fieldsHigh correlation
number_of_reviews_l30d is highly overall correlated with number_of_reviews and 1 other fieldsHigh correlation
calculated_host_listings_count is highly overall correlated with calculated_host_listings_count_entire_homesHigh correlation
calculated_host_listings_count_entire_homes is highly overall correlated with calculated_host_listings_countHigh correlation
source is highly overall correlated with availability_30 and 3 other fieldsHigh correlation
host_verifications is highly overall correlated with host_has_profile_pic and 1 other fieldsHigh correlation
host_has_profile_pic is highly overall correlated with host_verifications and 1 other fieldsHigh correlation
host_identity_verified is highly overall correlated with host_verifications and 1 other fieldsHigh correlation
room_type is highly overall correlated with bathrooms_textHigh correlation
bathrooms_text is highly overall correlated with room_typeHigh correlation
source is highly imbalanced (57.1%)Imbalance
host_verifications is highly imbalanced (69.2%)Imbalance
host_has_profile_pic is highly imbalanced (88.5%)Imbalance
host_identity_verified is highly imbalanced (55.0%)Imbalance
room_type is highly imbalanced (59.9%)Imbalance
bathrooms_text is highly imbalanced (50.7%)Imbalance
has_availability is highly imbalanced (82.9%)Imbalance
minimum_minimum_nights is highly skewed (γ1 = 20.34541596)Skewed
id has unique valuesUnique
listing_url has unique valuesUnique
host_listings_count is an unsupported type, check if it needs cleaning or further analysisUnsupported
host_total_listings_count is an unsupported type, check if it needs cleaning or further analysisUnsupported
bedrooms is an unsupported type, check if it needs cleaning or further analysisUnsupported
beds is an unsupported type, check if it needs cleaning or further analysisUnsupported
review_scores_rating is an unsupported type, check if it needs cleaning or further analysisUnsupported
review_scores_accuracy is an unsupported type, check if it needs cleaning or further analysisUnsupported
review_scores_cleanliness is an unsupported type, check if it needs cleaning or further analysisUnsupported
review_scores_checkin is an unsupported type, check if it needs cleaning or further analysisUnsupported
review_scores_communication is an unsupported type, check if it needs cleaning or further analysisUnsupported
review_scores_location is an unsupported type, check if it needs cleaning or further analysisUnsupported
review_scores_value is an unsupported type, check if it needs cleaning or further analysisUnsupported
reviews_per_month is an unsupported type, check if it needs cleaning or further analysisUnsupported
availability_30 has 3821 (19.5%) zerosZeros
availability_60 has 3149 (16.1%) zerosZeros
availability_90 has 2694 (13.7%) zerosZeros
availability_365 has 1752 (8.9%) zerosZeros
number_of_reviews has 5291 (27.0%) zerosZeros
number_of_reviews_ltm has 7356 (37.5%) zerosZeros
number_of_reviews_l30d has 13624 (69.5%) zerosZeros
calculated_host_listings_count_entire_homes has 2928 (14.9%) zerosZeros
calculated_host_listings_count_private_rooms has 14342 (73.2%) zerosZeros
calculated_host_listings_count_shared_rooms has 19063 (97.3%) zerosZeros

Reproduction

Analysis started2023-10-25 00:46:26.805339
Analysis finished2023-10-25 00:48:33.490812
Duration2 minutes and 6.69 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIQUE 

Distinct19595
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.406311 × 1017
Minimum17878
Maximum9.8555511 × 1017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:33.662075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17878
5-th percentile3000177.2
Q132865582
median6.2457727 × 1017
Q38.1437213 × 1017
95-th percentile9.5554878 × 1017
Maximum9.8555511 × 1017
Range9.8555511 × 1017
Interquartile range (IQR)8.1437213 × 1017

Descriptive statistics

Standard deviation4.0411467 × 1017
Coefficient of variation (CV)0.91712697
Kurtosis-1.838775
Mean4.406311 × 1017
Median Absolute Deviation (MAD)3.2838045 × 1017
Skewness-0.089006157
Sum1.0902249 × 1018
Variance1.6330867 × 1035
MonotonicityNot monotonic
2023-10-24T21:48:33.889119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
231497 1
 
< 0.1%
7.744843876 × 10171
 
< 0.1%
7.745524781 × 10171
 
< 0.1%
7.745491345 × 10171
 
< 0.1%
7.745008089 × 10171
 
< 0.1%
7.711880406 × 10171
 
< 0.1%
7.711878088 × 10171
 
< 0.1%
7.744955844 × 10171
 
< 0.1%
7.74491318 × 10171
 
< 0.1%
7.711768382 × 10171
 
< 0.1%
Other values (19585) 19585
99.9%
ValueCountFrequency (%)
17878 1
< 0.1%
25026 1
< 0.1%
35764 1
< 0.1%
48305 1
< 0.1%
48901 1
< 0.1%
49179 1
< 0.1%
53533 1
< 0.1%
60718 1
< 0.1%
65546 1
< 0.1%
66797 1
< 0.1%
ValueCountFrequency (%)
9.855551071 × 10171
< 0.1%
9.855103616 × 10171
< 0.1%
9.855076966 × 10171
< 0.1%
9.853409915 × 10171
< 0.1%
9.850642915 × 10171
< 0.1%
9.849783744 × 10171
< 0.1%
9.849547869 × 10171
< 0.1%
9.849146485 × 10171
< 0.1%
9.848409326 × 10171
< 0.1%
9.848131727 × 10171
< 0.1%

listing_url
Text

UNIQUE 

Distinct19595
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:34.133286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length47
Mean length42.440061
Min length34

Characters and Unicode

Total characters831613
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19595 ?
Unique (%)100.0%

Sample

1st rowhttps://www.airbnb.com/rooms/231497
2nd rowhttps://www.airbnb.com/rooms/231516
3rd rowhttps://www.airbnb.com/rooms/236991
4th rowhttps://www.airbnb.com/rooms/17878
5th rowhttps://www.airbnb.com/rooms/25026
ValueCountFrequency (%)
https://www.airbnb.com/rooms/231497 1
 
< 0.1%
https://www.airbnb.com/rooms/48901 1
 
< 0.1%
https://www.airbnb.com/rooms/25026 1
 
< 0.1%
https://www.airbnb.com/rooms/238802 1
 
< 0.1%
https://www.airbnb.com/rooms/239531 1
 
< 0.1%
https://www.airbnb.com/rooms/35764 1
 
< 0.1%
https://www.airbnb.com/rooms/245951 1
 
< 0.1%
https://www.airbnb.com/rooms/48305 1
 
< 0.1%
https://www.airbnb.com/rooms/247052 1
 
< 0.1%
https://www.airbnb.com/rooms/247779 1
 
< 0.1%
Other values (19585) 19585
99.9%
2023-10-24T21:48:34.558722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 78380
 
9.4%
o 58785
 
7.1%
w 58785
 
7.1%
m 39190
 
4.7%
s 39190
 
4.7%
. 39190
 
4.7%
t 39190
 
4.7%
r 39190
 
4.7%
b 39190
 
4.7%
8 27348
 
3.3%
Other values (16) 373175
44.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 431090
51.8%
Decimal Number 263358
31.7%
Other Punctuation 137165
 
16.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 58785
13.6%
w 58785
13.6%
m 39190
9.1%
s 39190
9.1%
t 39190
9.1%
r 39190
9.1%
b 39190
9.1%
h 19595
 
4.5%
n 19595
 
4.5%
i 19595
 
4.5%
Other values (3) 58785
13.6%
Decimal Number
ValueCountFrequency (%)
8 27348
10.4%
7 27298
10.4%
9 26811
10.2%
1 26439
10.0%
5 26353
10.0%
2 26167
9.9%
6 26156
9.9%
4 26061
9.9%
3 25940
9.8%
0 24785
9.4%
Other Punctuation
ValueCountFrequency (%)
/ 78380
57.1%
. 39190
28.6%
: 19595
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 431090
51.8%
Common 400523
48.2%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 78380
19.6%
. 39190
9.8%
8 27348
 
6.8%
7 27298
 
6.8%
9 26811
 
6.7%
1 26439
 
6.6%
5 26353
 
6.6%
2 26167
 
6.5%
6 26156
 
6.5%
4 26061
 
6.5%
Other values (3) 70320
17.6%
Latin
ValueCountFrequency (%)
o 58785
13.6%
w 58785
13.6%
m 39190
9.1%
s 39190
9.1%
t 39190
9.1%
r 39190
9.1%
b 39190
9.1%
h 19595
 
4.5%
n 19595
 
4.5%
i 19595
 
4.5%
Other values (3) 58785
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 831613
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 78380
 
9.4%
o 58785
 
7.1%
w 58785
 
7.1%
m 39190
 
4.7%
s 39190
 
4.7%
. 39190
 
4.7%
t 39190
 
4.7%
r 39190
 
4.7%
b 39190
 
4.7%
8 27348
 
3.3%
Other values (16) 373175
44.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size306.2 KiB
Minimum2023-09-22 00:00:00
Maximum2023-09-23 00:00:00
2023-10-24T21:48:34.747261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-24T21:48:34.891557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

source
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size306.2 KiB
city scrape
17875 
previous scrape
 
1720

Length

Max length15
Median length11
Mean length11.35111
Min length11

Characters and Unicode

Total characters222425
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcity scrape
2nd rowcity scrape
3rd rowcity scrape
4th rowcity scrape
5th rowcity scrape

Common Values

ValueCountFrequency (%)
city scrape 17875
91.2%
previous scrape 1720
 
8.8%

Length

2023-10-24T21:48:35.080377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-24T21:48:35.268939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
scrape 19595
50.0%
city 17875
45.6%
previous 1720
 
4.4%

Most occurring characters

ValueCountFrequency (%)
c 37470
16.8%
s 21315
9.6%
r 21315
9.6%
p 21315
9.6%
e 21315
9.6%
i 19595
8.8%
19595
8.8%
a 19595
8.8%
t 17875
8.0%
y 17875
8.0%
Other values (3) 5160
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 202830
91.2%
Space Separator 19595
 
8.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 37470
18.5%
s 21315
10.5%
r 21315
10.5%
p 21315
10.5%
e 21315
10.5%
i 19595
9.7%
a 19595
9.7%
t 17875
8.8%
y 17875
8.8%
v 1720
 
0.8%
Other values (2) 3440
 
1.7%
Space Separator
ValueCountFrequency (%)
19595
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 202830
91.2%
Common 19595
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 37470
18.5%
s 21315
10.5%
r 21315
10.5%
p 21315
10.5%
e 21315
10.5%
i 19595
9.7%
a 19595
9.7%
t 17875
8.8%
y 17875
8.8%
v 1720
 
0.8%
Other values (2) 3440
 
1.7%
Common
ValueCountFrequency (%)
19595
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 222425
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 37470
16.8%
s 21315
9.6%
r 21315
9.6%
p 21315
9.6%
e 21315
9.6%
i 19595
8.8%
19595
8.8%
a 19595
8.8%
t 17875
8.0%
y 17875
8.0%
Other values (3) 5160
 
2.3%

name
Text

Distinct8325
Distinct (%)42.5%
Missing0
Missing (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:35.575199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length168
Median length93
Mean length63.299923
Min length31

Characters and Unicode

Total characters1240362
Distinct characters84
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6511 ?
Unique (%)33.2%

Sample

1st rowRental unit in Rio de Janeiro · ★4.73 · 1 bedroom · 1 bed · 1 bath
2nd rowRental unit in Rio de Janeiro · ★4.71 · 1 bedroom · 1 bed
3rd rowRental unit in Rio de Janeiro · ★4.89 · 1 bedroom · 4 beds · 1 bath
4th rowCondo in Rio de Janeiro · ★4.70 · 2 bedrooms · 2 beds · 1 bath
5th rowRental unit in Rio de Janeiro · ★4.71 · 1 bedroom · 1 bed · 1 bath
ValueCountFrequency (%)
· 71010
23.2%
1 29274
 
9.5%
in 19595
 
6.4%
2 15256
 
5.0%
rental 14595
 
4.8%
unit 14595
 
4.8%
rio 13425
 
4.4%
de 13095
 
4.3%
janeiro 13044
 
4.3%
beds 12848
 
4.2%
Other values (425) 89865
29.3%
2023-10-24T21:48:36.158610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
287086
23.1%
e 92429
 
7.5%
o 75335
 
6.1%
· 71010
 
5.7%
a 69278
 
5.6%
n 68854
 
5.6%
i 65460
 
5.3%
b 60862
 
4.9%
d 58010
 
4.7%
t 54703
 
4.4%
Other values (74) 337335
27.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 711017
57.3%
Space Separator 287086
23.1%
Decimal Number 89584
 
7.2%
Other Punctuation 84208
 
6.8%
Uppercase Letter 56077
 
4.5%
Other Symbol 12320
 
1.0%
Dash Punctuation 55
 
< 0.1%
Open Punctuation 5
 
< 0.1%
Close Punctuation 5
 
< 0.1%
Other Letter 5
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 92429
13.0%
o 75335
10.6%
a 69278
9.7%
n 68854
9.7%
i 65460
9.2%
b 60862
8.6%
d 58010
8.2%
t 54703
7.7%
r 40050
5.6%
s 32516
 
4.6%
Other values (26) 93520
13.2%
Uppercase Letter
ValueCountFrequency (%)
R 28396
50.6%
J 13352
23.8%
C 4107
 
7.3%
H 1761
 
3.1%
B 1594
 
2.8%
N 1573
 
2.8%
S 1520
 
2.7%
T 974
 
1.7%
L 952
 
1.7%
I 621
 
1.1%
Other values (13) 1227
 
2.2%
Decimal Number
ValueCountFrequency (%)
1 31364
35.0%
2 16765
18.7%
4 10830
 
12.1%
5 8025
 
9.0%
3 8001
 
8.9%
0 4478
 
5.0%
8 2968
 
3.3%
9 2520
 
2.8%
7 2462
 
2.7%
6 2171
 
2.4%
Other Punctuation
ValueCountFrequency (%)
· 71010
84.3%
. 13138
 
15.6%
, 54
 
0.1%
/ 5
 
< 0.1%
: 1
 
< 0.1%
Other Letter
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Space Separator
ValueCountFrequency (%)
287086
100.0%
Other Symbol
ValueCountFrequency (%)
12320
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 55
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 767094
61.8%
Common 473263
38.2%
Han 5
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 92429
12.0%
o 75335
9.8%
a 69278
9.0%
n 68854
9.0%
i 65460
8.5%
b 60862
7.9%
d 58010
 
7.6%
t 54703
 
7.1%
r 40050
 
5.2%
s 32516
 
4.2%
Other values (49) 149597
19.5%
Common
ValueCountFrequency (%)
287086
60.7%
· 71010
 
15.0%
1 31364
 
6.6%
2 16765
 
3.5%
. 13138
 
2.8%
12320
 
2.6%
4 10830
 
2.3%
5 8025
 
1.7%
3 8001
 
1.7%
0 4478
 
0.9%
Other values (10) 10246
 
2.2%
Han
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1156461
93.2%
None 71576
 
5.8%
Misc Symbols 12320
 
1.0%
CJK 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
287086
24.8%
e 92429
 
8.0%
o 75335
 
6.5%
a 69278
 
6.0%
n 68854
 
6.0%
i 65460
 
5.7%
b 60862
 
5.3%
d 58010
 
5.0%
t 54703
 
4.7%
r 40050
 
3.5%
Other values (57) 284394
24.6%
None
ValueCountFrequency (%)
· 71010
99.2%
á 347
 
0.5%
ó 81
 
0.1%
ã 64
 
0.1%
â 38
 
0.1%
é 12
 
< 0.1%
í 10
 
< 0.1%
ú 7
 
< 0.1%
ç 4
 
< 0.1%
ê 2
 
< 0.1%
Misc Symbols
ValueCountFrequency (%)
12320
100.0%
CJK
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Distinct18101
Distinct (%)92.4%
Missing0
Missing (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:36.530278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length970
Median length744
Mean length518.51666
Min length0

Characters and Unicode

Total characters10160334
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17843 ?
Unique (%)91.1%

Sample

1st rowthis is a big studio at the end of copacabana walking distance to arpoador and ipanema which can accomodate persons in a double bed and another on couches which can be opened to sleep onclose to commercial area and public transportationthe spacespacious flat with double bed air conditioner tv linen ceiling fan small kitchen and bathroom there is also sofabed which can accommodate more people for an additional charge of us each hour doorman walking distance to ipanema the flat is very well located near the commercial area supermarkets restaurants banks bars and night clubs the very end of copacabana is a much better area to stay due to its proximity to ipanema you also have means of transport to anywhere in rio buses subway theres is a subway station two blocks away from the flat and taxis you are very close to the fortress of copacabana a main touristic attraction where you ha
2nd rowspecial location of the building on copacabana beach although the apartment does not have an ocean view but its great for people who love the sea and for the ones staying for new years accomodates up to personsthe spacespacious apartment with one bedroom comfortable double bed air conditioner tv linenlivingroom with a double sofabed more people can be accommodated small kitchen bathroom ceiling fans hour doorman walking distance to ipanema the building is on copacabana beach but the apartment does not have bech view its at the back of the building facing the street behindthe apartment is very well located near the commercial area supermarkets restaurants banks bars and night clubs the very end of copacabana is a much better area to stay due to its proximity to ipanema you are very close to the fortress of copacabana a main tourist attraction where you have the most fantastic view of the famous copacab
3rd rowaconchegante amplo bsico arejado iluminado com luz natural em prdio seguro e familiar prdio com portaria horas e cameras de segurana em todos os andares do edifcio tudo isto em copacabana a quase quadra do mar o segundo prdio da segunda quadra da praia est localizado na av prado junior quase esquina com av nsra de copacabanathe spaceo apartamento possui moblia bsica mas a necessria para voce se sentir em um espao limpo confortvel e aconchegante tambm tem os eletrodomesticos bsicos que no podem faltar em um apto como microondas cafeteira eltrica mquina de lavar fogo tv e geladeira todos a volts e um guardaroupas grande onde voc pode colocar suas malas roupas e pertences na sala h uma mesa com cadeiras um sof cama casal tipo fouton no quarto uma cama box de casal ortobom master pocket de molas ensacadas e camas de solteiro uma delas ortobom e bicama o apto tambm tem ar condicionado e ventil
4th rowplease note that elevated rates applies for new years and carnival price depends on length of stay and number of people generally i prefer a stay for week or more and a maximum of people at the most contact me and we will discuss bright and sunny large balcony square meters high speed wifi up to mb smart tv you can watch netflix etc if you have an account h doorman minute to walk to copacabana beach silent split air conditioning best spot in riothe space beautiful sunny bedroom square meters in h doorman building min to walk to copacabana beach spacious living room bedrooms with fullsize beds each sleeps large balcony which looks out on pedestrian street no traffic priceless in rio apts with sea view are noisy because of traffic split air condition in each room almost silent like in a hotel smart t
5th rowfully renovated in dec new kitchen new bathroom new flooring o apto foi todo renovado piso banheiro e cozinha novos em dez se vc nao tem opiniario no airbnb e nunca usou antes por favor mande mensagem antes falando quem vc our apartment is a little gem everyone loves staying there best location blocks to the subway blocks to the beach close to bars restaurants supermarkets subway wifi cable tv air con and fanthe spacethis newly renovated studio fully renovated dec is in the best location of copacabana situated on a quieter street but just off the main streets right in the middle of everything blocks from the beach block from the subway cantagalo station which places you just a stop away from ipanema you can just walk there too no need to hop on the subway really very close to all local bars and restaurants and very close to ipanema and lagoa walking dis
ValueCountFrequency (%)
de 67338
 
4.1%
e 64840
 
3.9%
a 40594
 
2.5%
com 35817
 
2.2%
the 22656
 
1.4%
and 21534
 
1.3%
da 21085
 
1.3%
do 20973
 
1.3%
para 20478
 
1.2%
o 16745
 
1.0%
Other values (59258) 1315627
79.8%
2023-10-24T21:48:37.172557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1720285
16.9%
a 1136007
11.2%
e 917043
 
9.0%
o 864962
 
8.5%
r 598552
 
5.9%
s 564500
 
5.6%
i 553159
 
5.4%
t 532041
 
5.2%
n 466047
 
4.6%
c 398838
 
3.9%
Other values (19) 2408900
23.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8439811
83.1%
Space Separator 1720479
 
16.9%
Control 44
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1136007
13.5%
e 917043
10.9%
o 864962
10.2%
r 598552
 
7.1%
s 564500
 
6.7%
i 553159
 
6.6%
t 532041
 
6.3%
n 466047
 
5.5%
c 398838
 
4.7%
d 396336
 
4.7%
Other values (16) 2012326
23.8%
Space Separator
ValueCountFrequency (%)
1720285
> 99.9%
  194
 
< 0.1%
Control
ValueCountFrequency (%)
44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8439811
83.1%
Common 1720523
 
16.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1136007
13.5%
e 917043
10.9%
o 864962
10.2%
r 598552
 
7.1%
s 564500
 
6.7%
i 553159
 
6.6%
t 532041
 
6.3%
n 466047
 
5.5%
c 398838
 
4.7%
d 396336
 
4.7%
Other values (16) 2012326
23.8%
Common
ValueCountFrequency (%)
1720285
> 99.9%
  194
 
< 0.1%
44
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10160140
> 99.9%
None 194
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1720285
16.9%
a 1136007
11.2%
e 917043
 
9.0%
o 864962
 
8.5%
r 598552
 
5.9%
s 564500
 
5.6%
i 553159
 
5.4%
t 532041
 
5.2%
n 466047
 
4.6%
c 398838
 
3.9%
Other values (18) 2408706
23.7%
None
ValueCountFrequency (%)
  194
100.0%
Distinct8646
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:37.556663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1000
Median length7
Mean length165.81153
Min length1

Characters and Unicode

Total characters3249077
Distinct characters260
Distinct categories21 ?
Distinct scripts3 ?
Distinct blocks15 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8202 ?
Unique (%)41.9%

Sample

1st rowno_info
2nd rowno_info
3rd rowCopacabana, apelidada a princesinha do mar, faz juz ao apelido.<br />Além de possuir uma das praias mais famosas e charmosas do Rio de Janeiro fornece ao turista ampla estrutura com variedade de restaurantes, agências de turismos, casas de câmbio, supermercados, drogarias, e a poucos passos um grande shopping (Rio Sul) e etc.
4th rowThis is the one of the bests spots in Rio. Because of the large balcony and proximity to the beach, it has huge advantages in the current situation.
5th rowCopacabana is a lively neighborhood and the apartment is located very close to an area in Copa full of bars, cafes and restaurants at Rua Bolivar and Domingos Ferreira. Copacabana never sleeps, there is always movement and it's a great mix of all kinds of people.
ValueCountFrequency (%)
de 19637
 
3.7%
e 16662
 
3.1%
a 13192
 
2.5%
the 11634
 
2.2%
do 11241
 
2.1%
no_info 9827
 
1.8%
da 8935
 
1.7%
o 8434
 
1.6%
and 7139
 
1.3%
é 6030
 
1.1%
Other values (20751) 424244
79.0%
2023-10-24T21:48:38.241438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
521367
16.0%
a 320223
 
9.9%
o 259263
 
8.0%
e 247976
 
7.6%
r 192870
 
5.9%
i 175865
 
5.4%
s 170853
 
5.3%
n 151702
 
4.7%
t 139905
 
4.3%
d 113943
 
3.5%
Other values (250) 955110
29.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2461826
75.8%
Space Separator 521652
 
16.1%
Uppercase Letter 115041
 
3.5%
Other Punctuation 94084
 
2.9%
Math Symbol 23051
 
0.7%
Decimal Number 14224
 
0.4%
Connector Punctuation 9911
 
0.3%
Dash Punctuation 3332
 
0.1%
Open Punctuation 2481
 
0.1%
Close Punctuation 2479
 
0.1%
Other values (11) 996
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 320223
13.0%
o 259263
10.5%
e 247976
10.1%
r 192870
 
7.8%
i 175865
 
7.1%
s 170853
 
6.9%
n 151702
 
6.2%
t 139905
 
5.7%
d 113943
 
4.6%
m 89263
 
3.6%
Other values (64) 599963
24.4%
Other Symbol
ValueCountFrequency (%)
10
 
7.0%
🏖 9
 
6.3%
° 8
 
5.6%
8
 
5.6%
7
 
4.9%
6
 
4.2%
6
 
4.2%
6
 
4.2%
5
 
3.5%
🏝 5
 
3.5%
Other values (52) 73
51.0%
Uppercase Letter
ValueCountFrequency (%)
C 11603
 
10.1%
R 10436
 
9.1%
A 10139
 
8.8%
B 8209
 
7.1%
P 7938
 
6.9%
S 7515
 
6.5%
O 6392
 
5.6%
T 6370
 
5.5%
I 6195
 
5.4%
L 5819
 
5.1%
Other values (40) 34425
29.9%
Other Punctuation
ValueCountFrequency (%)
, 47537
50.5%
. 27129
28.8%
/ 12171
 
12.9%
! 2268
 
2.4%
: 1513
 
1.6%
" 1085
 
1.2%
' 1024
 
1.1%
; 603
 
0.6%
* 262
 
0.3%
146
 
0.2%
Other values (9) 346
 
0.4%
Decimal Number
ValueCountFrequency (%)
0 3412
24.0%
2 2413
17.0%
1 2384
16.8%
5 1918
13.5%
4 1317
 
9.3%
3 985
 
6.9%
6 492
 
3.5%
8 433
 
3.0%
7 430
 
3.0%
9 422
 
3.0%
Other values (3) 18
 
0.1%
Math Symbol
ValueCountFrequency (%)
> 11538
50.1%
< 11442
49.6%
+ 30
 
0.1%
= 22
 
0.1%
10
 
< 0.1%
| 8
 
< 0.1%
~ 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 3175
95.3%
120
 
3.6%
36
 
1.1%
1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 2441
98.5%
] 36
 
1.5%
} 1
 
< 0.1%
1
 
< 0.1%
Modifier Symbol
ValueCountFrequency (%)
´ 71
81.6%
` 13
 
14.9%
^ 2
 
2.3%
🏻 1
 
1.1%
Open Punctuation
ValueCountFrequency (%)
( 2441
98.4%
[ 38
 
1.5%
2
 
0.1%
Final Punctuation
ValueCountFrequency (%)
186
49.5%
186
49.5%
» 4
 
1.1%
Nonspacing Mark
ValueCountFrequency (%)
21
77.8%
́ 4
 
14.8%
̃ 2
 
7.4%
Space Separator
ValueCountFrequency (%)
521367
99.9%
  285
 
0.1%
Initial Punctuation
ValueCountFrequency (%)
205
93.6%
14
 
6.4%
Format
ValueCountFrequency (%)
50
96.2%
2
 
3.8%
Control
ValueCountFrequency (%)
20
74.1%
 7
 
25.9%
Other Letter
ValueCountFrequency (%)
º 14
70.0%
ª 6
30.0%
Connector Punctuation
ValueCountFrequency (%)
_ 9911
100.0%
Other Number
ValueCountFrequency (%)
² 30
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 11
100.0%
Modifier Letter
ValueCountFrequency (%)
ˈ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2576377
79.3%
Common 672671
 
20.7%
Inherited 29
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
521367
77.5%
, 47537
 
7.1%
. 27129
 
4.0%
/ 12171
 
1.8%
> 11538
 
1.7%
< 11442
 
1.7%
_ 9911
 
1.5%
0 3412
 
0.5%
- 3175
 
0.5%
( 2441
 
0.4%
Other values (150) 22548
 
3.4%
Latin
ValueCountFrequency (%)
a 320223
12.4%
o 259263
 
10.1%
e 247976
 
9.6%
r 192870
 
7.5%
i 175865
 
6.8%
s 170853
 
6.6%
n 151702
 
5.9%
t 139905
 
5.4%
d 113943
 
4.4%
m 89263
 
3.5%
Other values (86) 714514
27.7%
Inherited
ValueCountFrequency (%)
21
72.4%
́ 4
 
13.8%
2
 
6.9%
̃ 2
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3192959
98.3%
None 54442
 
1.7%
Punctuation 1022
 
< 0.1%
Math Alphanum 528
 
< 0.1%
Dingbats 26
 
< 0.1%
Misc Symbols 26
 
< 0.1%
VS 21
 
< 0.1%
Geometric Shapes 12
 
< 0.1%
Arrows 10
 
< 0.1%
Emoticons 9
 
< 0.1%
Other values (5) 22
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
521367
16.3%
a 320223
 
10.0%
o 259263
 
8.1%
e 247976
 
7.8%
r 192870
 
6.0%
i 175865
 
5.5%
s 170853
 
5.4%
n 151702
 
4.8%
t 139905
 
4.4%
d 113943
 
3.6%
Other values (86) 898992
28.2%
None
ValueCountFrequency (%)
é 11889
21.8%
á 7852
14.4%
ã 7044
12.9%
ç 6963
12.8%
ó 4343
 
8.0%
í 4083
 
7.5%
ô 2638
 
4.8%
ê 2038
 
3.7%
õ 1822
 
3.3%
ú 1706
 
3.1%
Other values (76) 4064
 
7.5%
Punctuation
ValueCountFrequency (%)
205
20.1%
186
18.2%
186
18.2%
146
14.3%
120
11.7%
74
 
7.2%
50
 
4.9%
36
 
3.5%
14
 
1.4%
2
 
0.2%
Other values (2) 3
 
0.3%
Math Alphanum
ValueCountFrequency (%)
𝐚 87
16.5%
𝐞 51
 
9.7%
𝐫 42
 
8.0%
𝐭 30
 
5.7%
𝐨 30
 
5.7%
𝐧 30
 
5.7%
𝐢 27
 
5.1%
𝐜 21
 
4.0%
𝐡 21
 
4.0%
𝐮 21
 
4.0%
Other values (23) 168
31.8%
VS
ValueCountFrequency (%)
21
100.0%
Dingbats
ValueCountFrequency (%)
10
38.5%
8
30.8%
6
23.1%
1
 
3.8%
1
 
3.8%
Arrows
ValueCountFrequency (%)
10
100.0%
Misc Symbols
ValueCountFrequency (%)
7
26.9%
6
23.1%
5
19.2%
3
11.5%
1
 
3.8%
1
 
3.8%
1
 
3.8%
1
 
3.8%
1
 
3.8%
Geometric Shapes
ValueCountFrequency (%)
6
50.0%
3
25.0%
3
25.0%
IPA Ext
ValueCountFrequency (%)
ɐ 6
75.0%
ɾ 2
 
25.0%
Diacriticals
ValueCountFrequency (%)
́ 4
66.7%
̃ 2
33.3%
Modifier Letters
ValueCountFrequency (%)
ˈ 4
100.0%
Emoticons
ValueCountFrequency (%)
😀 2
22.2%
😊 2
22.2%
😉 2
22.2%
😄 1
11.1%
😎 1
11.1%
😍 1
11.1%
Specials
ValueCountFrequency (%)
2
100.0%
Enclosed Alphanum Sup
ValueCountFrequency (%)
🇧 1
50.0%
🇷 1
50.0%
Distinct19238
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:38.546733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length126
Median length125
Mean length92.081245
Min length61

Characters and Unicode

Total characters1804332
Distinct characters42
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18999 ?
Unique (%)97.0%

Sample

1st rowhttps://a0.muscache.com/pictures/3582382/ee8acc55_original.jpg
2nd rowhttps://a0.muscache.com/pictures/3671683/d74b44a4_original.jpg
3rd rowhttps://a0.muscache.com/pictures/5725a59b-147d-4bf2-99f2-ba67f55ee770.jpg
4th rowhttps://a0.muscache.com/pictures/65320518/30698f38_original.jpg
5th rowhttps://a0.muscache.com/pictures/a745aa21-b8dd-4959-a040-eb8e6e6f07ee.jpg
ValueCountFrequency (%)
https://a0.muscache.com/pictures/miso/hosting-666522668137624620/original/b31cbbbe-504b-4f42-9e6b-97b4532fef05.jpeg 18
 
0.1%
https://a0.muscache.com/pictures/miso/hosting-725373355070225589/original/f3a45b30-4e6e-45db-bec3-0af0cd1699e6.jpeg 12
 
0.1%
https://a0.muscache.com/pictures/dc1b2280-007c-426f-ba40-e6288979c77a.jpg 12
 
0.1%
https://a0.muscache.com/pictures/568254cd-a9c4-4cb9-bbfc-fa7403db3c2a.jpg 6
 
< 0.1%
https://a0.muscache.com/pictures/4638dc99-3002-484c-a63b-223a994f980a.jpg 6
 
< 0.1%
https://a0.muscache.com/pictures/miso/hosting-649254747332092573/original/7a335b5c-243b-4582-81c3-6d29336fe2c4.jpeg 5
 
< 0.1%
https://a0.muscache.com/pictures/28176be0-f475-41b6-9209-cf86dd14420b.jpg 5
 
< 0.1%
https://a0.muscache.com/pictures/miso/hosting-48093613/original/712ebac1-ef9c-4bd5-bfcd-64bd95382fd0.jpeg 5
 
< 0.1%
https://a0.muscache.com/pictures/miso/hosting-943516443559063800/original/38febba5-69b8-496b-9c30-cb9a89a8565f.jpeg 5
 
< 0.1%
https://a0.muscache.com/pictures/miso/hosting-49231675/original/8b68ced1-db1f-4a6d-aa7b-885852288c28.jpeg 5
 
< 0.1%
Other values (19228) 19516
99.6%
2023-10-24T21:48:39.044009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 114347
 
6.3%
/ 107524
 
6.0%
a 90634
 
5.0%
- 85671
 
4.7%
e 84197
 
4.7%
s 77529
 
4.3%
t 69720
 
3.9%
0 69440
 
3.8%
4 69125
 
3.8%
p 60936
 
3.4%
Other values (32) 975209
54.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 965971
53.5%
Decimal Number 556441
30.8%
Other Punctuation 185901
 
10.3%
Dash Punctuation 85671
 
4.7%
Uppercase Letter 9510
 
0.5%
Connector Punctuation 838
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 114347
11.8%
a 90634
 
9.4%
e 84197
 
8.7%
s 77529
 
8.0%
t 69720
 
7.2%
p 60936
 
6.3%
i 58969
 
6.1%
o 49869
 
5.2%
m 47040
 
4.9%
h 40660
 
4.2%
Other values (11) 272070
28.2%
Decimal Number
ValueCountFrequency (%)
0 69440
12.5%
4 69125
12.4%
8 56484
10.2%
9 56039
10.1%
7 51724
9.3%
6 51429
9.2%
5 50989
9.2%
3 50656
9.1%
2 50355
9.0%
1 50200
9.0%
Uppercase Letter
ValueCountFrequency (%)
H 9443
99.3%
J 16
 
0.2%
P 16
 
0.2%
E 16
 
0.2%
G 16
 
0.2%
S 3
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 107524
57.8%
. 58782
31.6%
: 19595
 
10.5%
Dash Punctuation
ValueCountFrequency (%)
- 85671
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 838
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 975481
54.1%
Common 828851
45.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 114347
11.7%
a 90634
 
9.3%
e 84197
 
8.6%
s 77529
 
7.9%
t 69720
 
7.1%
p 60936
 
6.2%
i 58969
 
6.0%
o 49869
 
5.1%
m 47040
 
4.8%
h 40660
 
4.2%
Other values (17) 281580
28.9%
Common
ValueCountFrequency (%)
/ 107524
13.0%
- 85671
10.3%
0 69440
8.4%
4 69125
8.3%
. 58782
 
7.1%
8 56484
 
6.8%
9 56039
 
6.8%
7 51724
 
6.2%
6 51429
 
6.2%
5 50989
 
6.2%
Other values (5) 171644
20.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1804332
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 114347
 
6.3%
/ 107524
 
6.0%
a 90634
 
5.0%
- 85671
 
4.7%
e 84197
 
4.7%
s 77529
 
4.3%
t 69720
 
3.9%
0 69440
 
3.8%
4 69125
 
3.8%
p 60936
 
3.4%
Other values (32) 975209
54.0%

host_id
Real number (ℝ)

Distinct12464
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7905336 × 108
Minimum1671
Maximum5.3798502 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:39.257489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1671
5-th percentile2513825
Q123670336
median98289727
Q33.3112819 × 108
95-th percentile4.9797966 × 108
Maximum5.3798502 × 108
Range5.3798335 × 108
Interquartile range (IQR)3.0745785 × 108

Descriptive statistics

Standard deviation1.7601965 × 108
Coefficient of variation (CV)0.98305694
Kurtosis-1.0259321
Mean1.7905336 × 108
Median Absolute Deviation (MAD)89313981
Skewness0.69757981
Sum3.5085505 × 1012
Variance3.0982916 × 1016
MonotonicityNot monotonic
2023-10-24T21:48:39.495011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6000862 159
 
0.8%
47584281 116
 
0.6%
74463624 77
 
0.4%
30165706 67
 
0.3%
459544 59
 
0.3%
12909867 58
 
0.3%
13580277 49
 
0.3%
91654021 46
 
0.2%
532498 46
 
0.2%
473492239 42
 
0.2%
Other values (12454) 18876
96.3%
ValueCountFrequency (%)
1671 1
 
< 0.1%
11739 7
< 0.1%
19065 1
 
< 0.1%
34105 1
 
< 0.1%
37072 1
 
< 0.1%
48024 7
< 0.1%
60098 1
 
< 0.1%
64036 2
 
< 0.1%
68997 1
 
< 0.1%
70933 8
< 0.1%
ValueCountFrequency (%)
537985018 1
< 0.1%
537957513 1
< 0.1%
537927925 1
< 0.1%
537736368 1
< 0.1%
537574642 1
< 0.1%
537538650 1
< 0.1%
537466326 1
< 0.1%
537324469 1
< 0.1%
537281388 1
< 0.1%
537241274 1
< 0.1%
Distinct12464
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:39.803169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length42
Mean length42.341363
Min length38

Characters and Unicode

Total characters829679
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10046 ?
Unique (%)51.3%

Sample

1st rowhttps://www.airbnb.com/users/show/1207700
2nd rowhttps://www.airbnb.com/users/show/1207700
3rd rowhttps://www.airbnb.com/users/show/1241662
4th rowhttps://www.airbnb.com/users/show/68997
5th rowhttps://www.airbnb.com/users/show/102840
ValueCountFrequency (%)
https://www.airbnb.com/users/show/6000862 159
 
0.8%
https://www.airbnb.com/users/show/47584281 116
 
0.6%
https://www.airbnb.com/users/show/74463624 77
 
0.4%
https://www.airbnb.com/users/show/30165706 67
 
0.3%
https://www.airbnb.com/users/show/459544 59
 
0.3%
https://www.airbnb.com/users/show/12909867 58
 
0.3%
https://www.airbnb.com/users/show/13580277 49
 
0.3%
https://www.airbnb.com/users/show/91654021 46
 
0.2%
https://www.airbnb.com/users/show/532498 46
 
0.2%
https://www.airbnb.com/users/show/473492239 42
 
0.2%
Other values (12454) 18876
96.3%
2023-10-24T21:48:40.355476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 97975
 
11.8%
s 78380
 
9.4%
w 78380
 
9.4%
h 39190
 
4.7%
r 39190
 
4.7%
t 39190
 
4.7%
b 39190
 
4.7%
o 39190
 
4.7%
. 39190
 
4.7%
a 19595
 
2.4%
Other values (18) 320209
38.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 509470
61.4%
Decimal Number 163449
 
19.7%
Other Punctuation 156760
 
18.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 78380
15.4%
w 78380
15.4%
h 39190
 
7.7%
r 39190
 
7.7%
t 39190
 
7.7%
b 39190
 
7.7%
o 39190
 
7.7%
a 19595
 
3.8%
p 19595
 
3.8%
i 19595
 
3.8%
Other values (5) 97975
19.2%
Decimal Number
ValueCountFrequency (%)
1 19300
11.8%
4 18418
11.3%
2 18117
11.1%
3 17338
10.6%
5 15741
9.6%
7 15318
9.4%
0 15087
9.2%
6 15065
9.2%
8 14820
9.1%
9 14245
8.7%
Other Punctuation
ValueCountFrequency (%)
/ 97975
62.5%
. 39190
 
25.0%
: 19595
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 509470
61.4%
Common 320209
38.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 78380
15.4%
w 78380
15.4%
h 39190
 
7.7%
r 39190
 
7.7%
t 39190
 
7.7%
b 39190
 
7.7%
o 39190
 
7.7%
a 19595
 
3.8%
p 19595
 
3.8%
i 19595
 
3.8%
Other values (5) 97975
19.2%
Common
ValueCountFrequency (%)
/ 97975
30.6%
. 39190
 
12.2%
: 19595
 
6.1%
1 19300
 
6.0%
4 18418
 
5.8%
2 18117
 
5.7%
3 17338
 
5.4%
5 15741
 
4.9%
7 15318
 
4.8%
0 15087
 
4.7%
Other values (3) 44130
13.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 829679
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 97975
 
11.8%
s 78380
 
9.4%
w 78380
 
9.4%
h 39190
 
4.7%
r 39190
 
4.7%
t 39190
 
4.7%
b 39190
 
4.7%
o 39190
 
4.7%
. 39190
 
4.7%
a 19595
 
2.4%
Other values (18) 320209
38.6%
Distinct4130
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:40.787099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length31
Mean length7.4017862
Min length1

Characters and Unicode

Total characters145038
Distinct characters96
Distinct categories14 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2377 ?
Unique (%)12.1%

Sample

1st rowMaria Luiza
2nd rowMaria Luiza
3rd rowNilda
4th rowMatthias
5th rowViviane
ValueCountFrequency (%)
maria 544
 
2.3%
rio 406
 
1.7%
ana 316
 
1.3%
carlos 265
 
1.1%
luiz 215
 
0.9%
renato 200
 
0.8%
paulo 192
 
0.8%
pedro 182
 
0.8%
do 175
 
0.7%
rodrigo 172
 
0.7%
Other values (3515) 21185
88.8%
2023-10-24T21:48:41.379505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 21858
15.1%
i 13569
 
9.4%
e 11476
 
7.9%
r 10330
 
7.1%
o 10032
 
6.9%
n 9732
 
6.7%
l 7916
 
5.5%
s 4874
 
3.4%
u 4283
 
3.0%
4275
 
2.9%
Other values (86) 46693
32.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 116384
80.2%
Uppercase Letter 24048
 
16.6%
Space Separator 4275
 
2.9%
Other Punctuation 214
 
0.1%
Decimal Number 62
 
< 0.1%
Dash Punctuation 36
 
< 0.1%
Other Symbol 4
 
< 0.1%
Other Letter 4
 
< 0.1%
Close Punctuation 3
 
< 0.1%
Open Punctuation 3
 
< 0.1%
Other values (4) 5
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 21858
18.8%
i 13569
11.7%
e 11476
9.9%
r 10330
8.9%
o 10032
8.6%
n 9732
8.4%
l 7916
 
6.8%
s 4874
 
4.2%
u 4283
 
3.7%
d 3870
 
3.3%
Other values (30) 18444
15.8%
Uppercase Letter
ValueCountFrequency (%)
M 2782
11.6%
R 2434
 
10.1%
A 2410
 
10.0%
C 1880
 
7.8%
L 1836
 
7.6%
S 1257
 
5.2%
J 1243
 
5.2%
D 1235
 
5.1%
F 1159
 
4.8%
G 1042
 
4.3%
Other values (21) 6770
28.2%
Decimal Number
ValueCountFrequency (%)
2 27
43.5%
0 10
 
16.1%
8 7
 
11.3%
1 6
 
9.7%
4 6
 
9.7%
3 2
 
3.2%
5 2
 
3.2%
6 2
 
3.2%
Other Punctuation
ValueCountFrequency (%)
& 171
79.9%
. 20
 
9.3%
/ 8
 
3.7%
, 8
 
3.7%
' 5
 
2.3%
@ 2
 
0.9%
Other Letter
ValueCountFrequency (%)
2
50.0%
2
50.0%
Space Separator
ValueCountFrequency (%)
4275
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 36
100.0%
Other Symbol
ValueCountFrequency (%)
® 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 1
100.0%
Final Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 140432
96.8%
Common 4602
 
3.2%
Han 4
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 21858
15.6%
i 13569
 
9.7%
e 11476
 
8.2%
r 10330
 
7.4%
o 10032
 
7.1%
n 9732
 
6.9%
l 7916
 
5.6%
s 4874
 
3.5%
u 4283
 
3.0%
d 3870
 
2.8%
Other values (61) 42492
30.3%
Common
ValueCountFrequency (%)
4275
92.9%
& 171
 
3.7%
- 36
 
0.8%
2 27
 
0.6%
. 20
 
0.4%
0 10
 
0.2%
/ 8
 
0.2%
, 8
 
0.2%
8 7
 
0.2%
1 6
 
0.1%
Other values (13) 34
 
0.7%
Han
ValueCountFrequency (%)
2
50.0%
2
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 143662
99.1%
None 1371
 
0.9%
CJK 4
 
< 0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 21858
15.2%
i 13569
 
9.4%
e 11476
 
8.0%
r 10330
 
7.2%
o 10032
 
7.0%
n 9732
 
6.8%
l 7916
 
5.5%
s 4874
 
3.4%
u 4283
 
3.0%
4275
 
3.0%
Other values (62) 45317
31.5%
None
ValueCountFrequency (%)
é 397
29.0%
á 299
21.8%
í 200
14.6%
ã 126
 
9.2%
ô 101
 
7.4%
ç 52
 
3.8%
ú 47
 
3.4%
ó 46
 
3.4%
â 29
 
2.1%
ê 23
 
1.7%
Other values (11) 51
 
3.7%
CJK
ValueCountFrequency (%)
2
50.0%
2
50.0%
Punctuation
ValueCountFrequency (%)
1
100.0%
Distinct3941
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:41.671805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9996938
Min length7

Characters and Unicode

Total characters195944
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique799 ?
Unique (%)4.1%

Sample

1st row2011-09-25
2nd row2011-09-25
3rd row2011-10-03
4th row2010-01-08
5th row2010-04-03
ValueCountFrequency (%)
2013-04-19 167
 
0.9%
2015-10-27 123
 
0.6%
2016-05-28 88
 
0.4%
2015-03-28 70
 
0.4%
2014-03-07 68
 
0.3%
2014-04-15 60
 
0.3%
2011-03-23 60
 
0.3%
2014-03-26 57
 
0.3%
2022-08-05 53
 
0.3%
2016-08-24 49
 
0.3%
Other values (3931) 18800
95.9%
2023-10-24T21:48:42.393024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 44115
22.5%
- 39186
20.0%
2 38966
19.9%
1 33389
17.0%
3 6897
 
3.5%
6 6602
 
3.4%
4 5878
 
3.0%
5 5734
 
2.9%
7 5219
 
2.7%
9 5020
 
2.6%
Other values (6) 4938
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 156744
80.0%
Dash Punctuation 39186
 
20.0%
Lowercase Letter 12
 
< 0.1%
Connector Punctuation 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 44115
28.1%
2 38966
24.9%
1 33389
21.3%
3 6897
 
4.4%
6 6602
 
4.2%
4 5878
 
3.8%
5 5734
 
3.7%
7 5219
 
3.3%
9 5020
 
3.2%
8 4924
 
3.1%
Lowercase Letter
ValueCountFrequency (%)
n 4
33.3%
o 4
33.3%
i 2
16.7%
f 2
16.7%
Dash Punctuation
ValueCountFrequency (%)
- 39186
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 195932
> 99.9%
Latin 12
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 44115
22.5%
- 39186
20.0%
2 38966
19.9%
1 33389
17.0%
3 6897
 
3.5%
6 6602
 
3.4%
4 5878
 
3.0%
5 5734
 
2.9%
7 5219
 
2.7%
9 5020
 
2.6%
Other values (2) 4926
 
2.5%
Latin
ValueCountFrequency (%)
n 4
33.3%
o 4
33.3%
i 2
16.7%
f 2
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 195944
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 44115
22.5%
- 39186
20.0%
2 38966
19.9%
1 33389
17.0%
3 6897
 
3.5%
6 6602
 
3.4%
4 5878
 
3.0%
5 5734
 
2.9%
7 5219
 
2.7%
9 5020
 
2.6%
Other values (6) 4938
 
2.5%
Distinct388
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:42.721127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length22
Mean length18.344527
Min length5

Characters and Unicode

Total characters359461
Distinct characters71
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique207 ?
Unique (%)1.1%

Sample

1st rowRio de Janeiro, Brazil
2nd rowRio de Janeiro, Brazil
3rd rowRio de Janeiro, Brazil
4th rowRio de Janeiro, Brazil
5th rowRio de Janeiro, Brazil
ValueCountFrequency (%)
brazil 14869
23.3%
rio 13376
21.0%
de 12904
20.3%
janeiro 12864
20.2%
no_info 4217
 
6.6%
of 1173
 
1.8%
state 1172
 
1.8%
são 297
 
0.5%
paulo 291
 
0.5%
brasília 76
 
0.1%
Other values (483) 2441
 
3.8%
2023-10-24T21:48:43.276694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 46559
13.0%
44085
12.3%
o 37591
10.5%
a 31006
8.6%
r 29001
 
8.1%
e 28118
 
7.8%
n 22212
 
6.2%
l 15842
 
4.4%
B 15161
 
4.2%
z 15029
 
4.2%
Other values (61) 74857
20.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 250680
69.7%
Uppercase Letter 45486
 
12.7%
Space Separator 44085
 
12.3%
Other Punctuation 14984
 
4.2%
Connector Punctuation 4217
 
1.2%
Dash Punctuation 9
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 46559
18.6%
o 37591
15.0%
a 31006
12.4%
r 29001
11.6%
e 28118
11.2%
n 22212
8.9%
l 15842
 
6.3%
z 15029
 
6.0%
d 13339
 
5.3%
f 5400
 
2.2%
Other values (28) 6583
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
B 15161
33.3%
R 13423
29.5%
J 12912
28.4%
S 1638
 
3.6%
P 487
 
1.1%
A 236
 
0.5%
F 221
 
0.5%
N 203
 
0.4%
C 189
 
0.4%
M 176
 
0.4%
Other values (17) 840
 
1.8%
Other Punctuation
ValueCountFrequency (%)
, 14982
> 99.9%
' 1
 
< 0.1%
. 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
44085
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4217
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 296166
82.4%
Common 63295
 
17.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 46559
15.7%
o 37591
12.7%
a 31006
10.5%
r 29001
9.8%
e 28118
9.5%
n 22212
7.5%
l 15842
 
5.3%
B 15161
 
5.1%
z 15029
 
5.1%
R 13423
 
4.5%
Other values (55) 42224
14.3%
Common
ValueCountFrequency (%)
44085
69.7%
, 14982
 
23.7%
_ 4217
 
6.7%
- 9
 
< 0.1%
' 1
 
< 0.1%
. 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 358741
99.8%
None 720
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 46559
13.0%
44085
12.3%
o 37591
10.5%
a 31006
8.6%
r 29001
 
8.1%
e 28118
 
7.8%
n 22212
 
6.2%
l 15842
 
4.4%
B 15161
 
4.2%
z 15029
 
4.2%
Other values (48) 74137
20.7%
None
ValueCountFrequency (%)
ã 323
44.9%
ó 116
 
16.1%
í 105
 
14.6%
ç 44
 
6.1%
é 40
 
5.6%
á 35
 
4.9%
ú 23
 
3.2%
â 20
 
2.8%
ü 8
 
1.1%
ê 3
 
0.4%
Other values (3) 3
 
0.4%
Distinct4858
Distinct (%)24.8%
Missing0
Missing (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:43.623308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6323
Median length7
Mean length173.96162
Min length1

Characters and Unicode

Total characters3408778
Distinct characters175
Distinct categories21 ?
Distinct scripts4 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3549 ?
Unique (%)18.1%

Sample

1st rowMeu nome é Maria Luiza, adoro ajudar meus hóspedes, pois vivi muito tempo no exterior, falo várias línguas, e entendo como é viver fora da sua cidade. Falo e escrevo em Inglês, francês, espanhol e português.
2nd rowMeu nome é Maria Luiza, adoro ajudar meus hóspedes, pois vivi muito tempo no exterior, falo várias línguas, e entendo como é viver fora da sua cidade. Falo e escrevo em Inglês, francês, espanhol e português.
3rd rowHellow ! Im Nilda! I love Rio de Janeiro. I work renting apartments for short time. the places are simples! but very clean , safe and well provided with basic staffs to spent a great vacations. Very well located, next to the beach one of the most famous Rio de Janeiro´ s beach: Copacabana you ll have easy and plenty access by bus and others public transportations services to the main and classic touristic points more visited by the travellers in Rio de Janeiro. Welcome to Rio, welcome to Brazil!
4th rowI am a journalist/writer. Lived in NYC for 15 years. I am now based in Rio and published 3 volumes of travel stories on AMAZ0N: "The World Is My Oyster". If you have never been to Rio, check out the first story, and you'll get an idea. Apart from Rio, you'll find 29 other travel stories from all around the globe.
5th rowHi guys, Viviane is a commercial photographer, an avid world traveler, (a former photographer for Airbnb) and an Airbnb superhost. And a free lance photographer for other wonderful clients. She loves life and meeting people. We work together in providing the best accommodation to people and we are firm believers of enjoying the moment as a prime attitude towards life!
ValueCountFrequency (%)
de 19506
 
3.5%
e 19046
 
3.4%
a 11751
 
2.1%
no_info 10033
 
1.8%
and 7895
 
1.4%
que 7061
 
1.3%
rio 6505
 
1.2%
para 5853
 
1.0%
5640
 
1.0%
to 5388
 
1.0%
Other values (18056) 462736
82.4%
2023-10-24T21:48:44.241631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
536779
15.7%
e 310785
 
9.1%
a 300595
 
8.8%
o 272713
 
8.0%
s 190587
 
5.6%
i 182487
 
5.4%
r 178950
 
5.2%
n 164313
 
4.8%
t 135087
 
4.0%
d 114910
 
3.4%
Other values (165) 1021572
30.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2597513
76.2%
Space Separator 536793
 
15.7%
Uppercase Letter 106625
 
3.1%
Other Punctuation 87775
 
2.6%
Control 36399
 
1.1%
Connector Punctuation 15986
 
0.5%
Decimal Number 14522
 
0.4%
Dash Punctuation 8048
 
0.2%
Close Punctuation 1835
 
0.1%
Open Punctuation 1318
 
< 0.1%
Other values (11) 1964
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 310785
12.0%
a 300595
11.6%
o 272713
10.5%
s 190587
 
7.3%
i 182487
 
7.0%
r 178950
 
6.9%
n 164313
 
6.3%
t 135087
 
5.2%
d 114910
 
4.4%
m 105986
 
4.1%
Other values (45) 641100
24.7%
Uppercase Letter
ValueCountFrequency (%)
I 9988
 
9.4%
R 9802
 
9.2%
A 9744
 
9.1%
S 9303
 
8.7%
E 6094
 
5.7%
C 5995
 
5.6%
M 5419
 
5.1%
J 5222
 
4.9%
B 5158
 
4.8%
T 5016
 
4.7%
Other values (31) 34884
32.7%
Other Punctuation
ValueCountFrequency (%)
, 37939
43.2%
. 30875
35.2%
! 9313
 
10.6%
: 2158
 
2.5%
' 2097
 
2.4%
/ 2059
 
2.3%
" 739
 
0.8%
? 663
 
0.8%
* 555
 
0.6%
¡ 490
 
0.6%
Other values (9) 887
 
1.0%
Other Symbol
ValueCountFrequency (%)
43
32.6%
° 36
27.3%
28
21.2%
6
 
4.5%
5
 
3.8%
3
 
2.3%
3
 
2.3%
2
 
1.5%
2
 
1.5%
© 2
 
1.5%
Other values (2) 2
 
1.5%
Decimal Number
ValueCountFrequency (%)
0 3572
24.6%
2 3162
21.8%
1 2387
16.4%
3 1056
 
7.3%
4 903
 
6.2%
5 793
 
5.5%
9 713
 
4.9%
8 655
 
4.5%
6 642
 
4.4%
7 639
 
4.4%
Math Symbol
ValueCountFrequency (%)
= 790
88.5%
+ 55
 
6.2%
> 19
 
2.1%
< 17
 
1.9%
| 10
 
1.1%
~ 2
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 1309
99.3%
[ 6
 
0.5%
2
 
0.2%
{ 1
 
0.1%
Modifier Symbol
ValueCountFrequency (%)
´ 313
94.0%
` 14
 
4.2%
^ 4
 
1.2%
¨ 2
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
- 8015
99.6%
27
 
0.3%
6
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 1819
99.1%
] 15
 
0.8%
} 1
 
0.1%
Nonspacing Mark
ValueCountFrequency (%)
67
97.1%
̂ 1
 
1.4%
̃ 1
 
1.4%
Space Separator
ValueCountFrequency (%)
536779
> 99.9%
  14
 
< 0.1%
Control
ValueCountFrequency (%)
29620
81.4%
6779
 
18.6%
Final Punctuation
ValueCountFrequency (%)
177
53.6%
153
46.4%
Initial Punctuation
ValueCountFrequency (%)
168
94.4%
10
 
5.6%
Other Letter
ValueCountFrequency (%)
ª 7
58.3%
º 5
41.7%
Connector Punctuation
ValueCountFrequency (%)
_ 15986
100.0%
Format
ValueCountFrequency (%)
6
100.0%
Private Use
ValueCountFrequency (%)
4
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 4
100.0%
Other Number
ValueCountFrequency (%)
² 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2704150
79.3%
Common 704555
 
20.7%
Inherited 69
 
< 0.1%
Unknown 4
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 310785
11.5%
a 300595
11.1%
o 272713
 
10.1%
s 190587
 
7.0%
i 182487
 
6.7%
r 178950
 
6.6%
n 164313
 
6.1%
t 135087
 
5.0%
d 114910
 
4.2%
m 105986
 
3.9%
Other values (88) 747737
27.7%
Common
ValueCountFrequency (%)
536779
76.2%
, 37939
 
5.4%
. 30875
 
4.4%
29620
 
4.2%
_ 15986
 
2.3%
! 9313
 
1.3%
- 8015
 
1.1%
6779
 
1.0%
0 3572
 
0.5%
2 3162
 
0.4%
Other values (63) 22515
 
3.2%
Inherited
ValueCountFrequency (%)
67
97.1%
̂ 1
 
1.4%
̃ 1
 
1.4%
Unknown
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3356571
98.5%
None 51396
 
1.5%
Punctuation 647
 
< 0.1%
Dingbats 76
 
< 0.1%
VS 67
 
< 0.1%
Misc Symbols 15
 
< 0.1%
PUA 4
 
< 0.1%
Diacriticals 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
536779
16.0%
e 310785
 
9.3%
a 300595
 
9.0%
o 272713
 
8.1%
s 190587
 
5.7%
i 182487
 
5.4%
r 178950
 
5.3%
n 164313
 
4.9%
t 135087
 
4.0%
d 114910
 
3.4%
Other values (87) 969365
28.9%
None
ValueCountFrequency (%)
á 7970
15.5%
ç 7687
15.0%
é 7206
14.0%
ã 7109
13.8%
ó 5977
11.6%
ê 4979
9.7%
í 4019
7.8%
õ 1656
 
3.2%
ú 1353
 
2.6%
à 775
 
1.5%
Other values (45) 2665
 
5.2%
Punctuation
ValueCountFrequency (%)
177
27.4%
168
26.0%
153
23.6%
75
11.6%
27
 
4.2%
23
 
3.6%
10
 
1.5%
6
 
0.9%
6
 
0.9%
2
 
0.3%
VS
ValueCountFrequency (%)
67
100.0%
Dingbats
ValueCountFrequency (%)
43
56.6%
28
36.8%
3
 
3.9%
2
 
2.6%
Misc Symbols
ValueCountFrequency (%)
6
40.0%
5
33.3%
2
 
13.3%
1
 
6.7%
1
 
6.7%
PUA
ValueCountFrequency (%)
4
100.0%
Diacriticals
ValueCountFrequency (%)
̂ 1
50.0%
̃ 1
50.0%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size306.2 KiB
within an hour
9495 
no_info
3624 
within a few hours
3368 
within a day
1907 
a few days or more
1201 

Length

Max length18
Median length14
Mean length13.443429
Min length7

Characters and Unicode

Total characters263424
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwithin a few hours
2nd rowwithin a few hours
3rd rowwithin an hour
4th rowwithin an hour
5th rowwithin a few hours

Common Values

ValueCountFrequency (%)
within an hour 9495
48.5%
no_info 3624
 
18.5%
within a few hours 3368
 
17.2%
within a day 1907
 
9.7%
a few days or more 1201
 
6.1%

Length

2023-10-24T21:48:44.442240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-24T21:48:44.608516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
within 14770
25.8%
an 9495
16.6%
hour 9495
16.6%
a 6476
11.3%
few 4569
 
8.0%
no_info 3624
 
6.3%
hours 3368
 
5.9%
day 1907
 
3.3%
days 1201
 
2.1%
or 1201
 
2.1%

Most occurring characters

ValueCountFrequency (%)
37712
14.3%
i 33164
12.6%
n 31513
12.0%
h 27633
10.5%
o 22513
8.5%
w 19339
7.3%
a 19079
7.2%
r 15265
5.8%
t 14770
 
5.6%
u 12863
 
4.9%
Other values (7) 29573
11.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 222088
84.3%
Space Separator 37712
 
14.3%
Connector Punctuation 3624
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 33164
14.9%
n 31513
14.2%
h 27633
12.4%
o 22513
10.1%
w 19339
8.7%
a 19079
8.6%
r 15265
6.9%
t 14770
6.7%
u 12863
 
5.8%
f 8193
 
3.7%
Other values (5) 17756
8.0%
Space Separator
ValueCountFrequency (%)
37712
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3624
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 222088
84.3%
Common 41336
 
15.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 33164
14.9%
n 31513
14.2%
h 27633
12.4%
o 22513
10.1%
w 19339
8.7%
a 19079
8.6%
r 15265
6.9%
t 14770
6.7%
u 12863
 
5.8%
f 8193
 
3.7%
Other values (5) 17756
8.0%
Common
ValueCountFrequency (%)
37712
91.2%
_ 3624
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 263424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
37712
14.3%
i 33164
12.6%
n 31513
12.0%
h 27633
10.5%
o 22513
8.5%
w 19339
7.3%
a 19079
7.2%
r 15265
5.8%
t 14770
 
5.6%
u 12863
 
4.9%
Other values (7) 29573
11.2%
Distinct92
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:44.861948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length4.2516969
Min length2

Characters and Unicode

Total characters83312
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row100%
2nd row100%
3rd row100%
4th row100%
5th row100%
ValueCountFrequency (%)
100 10709
54.7%
no_info 3624
 
18.5%
0 649
 
3.3%
90 393
 
2.0%
99 334
 
1.7%
97 246
 
1.3%
80 233
 
1.2%
50 230
 
1.2%
98 221
 
1.1%
96 204
 
1.0%
Other values (82) 2752
 
14.0%
2023-10-24T21:48:45.266205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 23354
28.0%
% 15971
19.2%
1 11071
13.3%
n 7248
 
8.7%
o 7248
 
8.7%
_ 3624
 
4.3%
i 3624
 
4.3%
f 3624
 
4.3%
9 2604
 
3.1%
8 1181
 
1.4%
Other values (6) 3763
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 41973
50.4%
Lowercase Letter 21744
26.1%
Other Punctuation 15971
 
19.2%
Connector Punctuation 3624
 
4.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23354
55.6%
1 11071
26.4%
9 2604
 
6.2%
8 1181
 
2.8%
7 1028
 
2.4%
6 765
 
1.8%
5 703
 
1.7%
3 498
 
1.2%
2 420
 
1.0%
4 349
 
0.8%
Lowercase Letter
ValueCountFrequency (%)
n 7248
33.3%
o 7248
33.3%
i 3624
16.7%
f 3624
16.7%
Other Punctuation
ValueCountFrequency (%)
% 15971
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3624
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 61568
73.9%
Latin 21744
 
26.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23354
37.9%
% 15971
25.9%
1 11071
18.0%
_ 3624
 
5.9%
9 2604
 
4.2%
8 1181
 
1.9%
7 1028
 
1.7%
6 765
 
1.2%
5 703
 
1.1%
3 498
 
0.8%
Other values (2) 769
 
1.2%
Latin
ValueCountFrequency (%)
n 7248
33.3%
o 7248
33.3%
i 3624
16.7%
f 3624
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 83312
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23354
28.0%
% 15971
19.2%
1 11071
13.3%
n 7248
 
8.7%
o 7248
 
8.7%
_ 3624
 
4.3%
i 3624
 
4.3%
f 3624
 
4.3%
9 2604
 
3.1%
8 1181
 
1.4%
Other values (6) 3763
 
4.5%
Distinct100
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:45.494847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.7639704
Min length2

Characters and Unicode

Total characters73755
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row82%
2nd row82%
3rd row96%
4th row96%
5th row73%
ValueCountFrequency (%)
100 5813
29.7%
no_info 2493
 
12.7%
99 855
 
4.4%
0 786
 
4.0%
98 602
 
3.1%
97 503
 
2.6%
96 475
 
2.4%
50 403
 
2.1%
94 391
 
2.0%
93 327
 
1.7%
Other values (90) 6947
35.5%
2023-10-24T21:48:45.935937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
% 17102
23.2%
0 13773
18.7%
1 6633
 
9.0%
9 5675
 
7.7%
n 4986
 
6.8%
o 4986
 
6.8%
8 3059
 
4.1%
7 2511
 
3.4%
_ 2493
 
3.4%
i 2493
 
3.4%
Other values (6) 10044
13.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39202
53.2%
Other Punctuation 17102
23.2%
Lowercase Letter 14958
 
20.3%
Connector Punctuation 2493
 
3.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13773
35.1%
1 6633
16.9%
9 5675
14.5%
8 3059
 
7.8%
7 2511
 
6.4%
6 2048
 
5.2%
5 1821
 
4.6%
3 1580
 
4.0%
4 1165
 
3.0%
2 937
 
2.4%
Lowercase Letter
ValueCountFrequency (%)
n 4986
33.3%
o 4986
33.3%
i 2493
16.7%
f 2493
16.7%
Other Punctuation
ValueCountFrequency (%)
% 17102
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2493
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 58797
79.7%
Latin 14958
 
20.3%

Most frequent character per script

Common
ValueCountFrequency (%)
% 17102
29.1%
0 13773
23.4%
1 6633
 
11.3%
9 5675
 
9.7%
8 3059
 
5.2%
7 2511
 
4.3%
_ 2493
 
4.2%
6 2048
 
3.5%
5 1821
 
3.1%
3 1580
 
2.7%
Other values (2) 2102
 
3.6%
Latin
ValueCountFrequency (%)
n 4986
33.3%
o 4986
33.3%
i 2493
16.7%
f 2493
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73755
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
% 17102
23.2%
0 13773
18.7%
1 6633
 
9.0%
9 5675
 
7.7%
n 4986
 
6.8%
o 4986
 
6.8%
8 3059
 
4.1%
7 2511
 
3.4%
_ 2493
 
3.4%
i 2493
 
3.4%
Other values (6) 10044
13.6%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size306.2 KiB
f
13849 
t
5285 
no_info
 
461

Length

Max length7
Median length1
Mean length1.1411585
Min length1

Characters and Unicode

Total characters22361
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowf
2nd rowf
3rd rowt
4th rowt
5th rowt

Common Values

ValueCountFrequency (%)
f 13849
70.7%
t 5285
 
27.0%
no_info 461
 
2.4%

Length

2023-10-24T21:48:46.155660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-24T21:48:46.326882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 13849
70.7%
t 5285
 
27.0%
no_info 461
 
2.4%

Most occurring characters

ValueCountFrequency (%)
f 14310
64.0%
t 5285
 
23.6%
n 922
 
4.1%
o 922
 
4.1%
_ 461
 
2.1%
i 461
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 21900
97.9%
Connector Punctuation 461
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 14310
65.3%
t 5285
 
24.1%
n 922
 
4.2%
o 922
 
4.2%
i 461
 
2.1%
Connector Punctuation
ValueCountFrequency (%)
_ 461
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 21900
97.9%
Common 461
 
2.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 14310
65.3%
t 5285
 
24.1%
n 922
 
4.2%
o 922
 
4.2%
i 461
 
2.1%
Common
ValueCountFrequency (%)
_ 461
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22361
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 14310
64.0%
t 5285
 
23.6%
n 922
 
4.1%
o 922
 
4.1%
_ 461
 
2.1%
i 461
 
2.1%
Distinct11997
Distinct (%)61.2%
Missing0
Missing (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:46.527553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length131
Median length106
Mean length106.96816
Min length7

Characters and Unicode

Total characters2096041
Distinct characters41
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9617 ?
Unique (%)49.1%

Sample

1st rowhttps://a0.muscache.com/im/users/1207700/profile_pic/1316987019/original.jpg?aki_policy=profile_small
2nd rowhttps://a0.muscache.com/im/users/1207700/profile_pic/1316987019/original.jpg?aki_policy=profile_small
3rd rowhttps://a0.muscache.com/im/pictures/user/fea78163-5495-401a-a620-ed948f59ac91.jpg?aki_policy=profile_small
4th rowhttps://a0.muscache.com/im/pictures/user/67b13cea-8c11-49c0-a08d-7f42c330676e.jpg?aki_policy=profile_small
5th rowhttps://a0.muscache.com/im/pictures/user/315ddc81-bea3-4bf0-8fc7-be197a6541ff.jpg?aki_policy=profile_small
ValueCountFrequency (%)
https://a0.muscache.com/defaults/user_pic-50x50.png?v=3 533
 
2.7%
https://a0.muscache.com/im/pictures/user/c1738b63-30f9-40ff-b53a-4ea0640c3beb.jpg?aki_policy=profile_small 159
 
0.8%
https://a0.muscache.com/im/pictures/user/cb326f23-ef4a-4fee-a71a-8584e5af6815.jpg?aki_policy=profile_small 116
 
0.6%
https://a0.muscache.com/im/pictures/user/f855bc7e-c7f4-4cb6-8cb5-d81d2a58336a.jpg?aki_policy=profile_small 77
 
0.4%
https://a0.muscache.com/im/pictures/user/da4143cd-6939-4e65-b412-4f7990518552.jpg?aki_policy=profile_small 67
 
0.3%
https://a0.muscache.com/im/pictures/user/69eb44d3-3b36-4743-9d01-92bad2fb4a83.jpg?aki_policy=profile_small 59
 
0.3%
https://a0.muscache.com/im/pictures/user/da4efd54-caaa-4203-970a-8aa4bae02f61.jpg?aki_policy=profile_small 58
 
0.3%
https://a0.muscache.com/im/pictures/user/user-13580277/original/51401555-7a4b-4067-89e3-3cb405420c16.jpeg?aki_policy=profile_small 49
 
0.3%
https://a0.muscache.com/im/pictures/user/57b501f7-d78e-4707-af93-dfacb7536fed.jpg?aki_policy=profile_small 46
 
0.2%
https://a0.muscache.com/im/users/532498/profile_pic/1369411907/original.jpg?aki_policy=profile_small 46
 
0.2%
Other values (11987) 18385
93.8%
2023-10-24T21:48:46.935168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 128972
 
6.2%
/ 124976
 
6.0%
a 118952
 
5.7%
e 114502
 
5.5%
i 106311
 
5.1%
s 99682
 
4.8%
p 98891
 
4.7%
l 83007
 
4.0%
m 77306
 
3.7%
- 71055
 
3.4%
Other values (31) 1072387
51.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1317551
62.9%
Decimal Number 422004
 
20.1%
Other Punctuation 222941
 
10.6%
Dash Punctuation 71055
 
3.4%
Connector Punctuation 40647
 
1.9%
Math Symbol 19593
 
0.9%
Uppercase Letter 2250
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 128972
 
9.8%
a 118952
 
9.0%
e 114502
 
8.7%
i 106311
 
8.1%
s 99682
 
7.6%
p 98891
 
7.5%
l 83007
 
6.3%
m 77306
 
5.9%
r 64205
 
4.9%
o 63951
 
4.9%
Other values (13) 361772
27.5%
Decimal Number
ValueCountFrequency (%)
0 57249
13.6%
4 55644
13.2%
9 40507
9.6%
8 40077
9.5%
3 39832
9.4%
1 39488
9.4%
5 39014
9.2%
2 37198
8.8%
7 36570
8.7%
6 36425
8.6%
Other Punctuation
ValueCountFrequency (%)
/ 124976
56.1%
. 58779
26.4%
? 19593
 
8.8%
: 19593
 
8.8%
Dash Punctuation
ValueCountFrequency (%)
- 71055
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 40647
100.0%
Math Symbol
ValueCountFrequency (%)
= 19593
100.0%
Uppercase Letter
ValueCountFrequency (%)
U 2250
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1319801
63.0%
Common 776240
37.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 128972
 
9.8%
a 118952
 
9.0%
e 114502
 
8.7%
i 106311
 
8.1%
s 99682
 
7.6%
p 98891
 
7.5%
l 83007
 
6.3%
m 77306
 
5.9%
r 64205
 
4.9%
o 63951
 
4.8%
Other values (14) 364022
27.6%
Common
ValueCountFrequency (%)
/ 124976
16.1%
- 71055
 
9.2%
. 58779
 
7.6%
0 57249
 
7.4%
4 55644
 
7.2%
_ 40647
 
5.2%
9 40507
 
5.2%
8 40077
 
5.2%
3 39832
 
5.1%
1 39488
 
5.1%
Other values (7) 207986
26.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2096041
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 128972
 
6.2%
/ 124976
 
6.0%
a 118952
 
5.7%
e 114502
 
5.5%
i 106311
 
5.1%
s 99682
 
4.8%
p 98891
 
4.7%
l 83007
 
4.0%
m 77306
 
3.7%
- 71055
 
3.4%
Other values (31) 1072387
51.2%
Distinct11997
Distinct (%)61.2%
Missing0
Missing (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:47.125253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length134
Median length109
Mean length109.94065
Min length7

Characters and Unicode

Total characters2154287
Distinct characters41
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9617 ?
Unique (%)49.1%

Sample

1st rowhttps://a0.muscache.com/im/users/1207700/profile_pic/1316987019/original.jpg?aki_policy=profile_x_medium
2nd rowhttps://a0.muscache.com/im/users/1207700/profile_pic/1316987019/original.jpg?aki_policy=profile_x_medium
3rd rowhttps://a0.muscache.com/im/pictures/user/fea78163-5495-401a-a620-ed948f59ac91.jpg?aki_policy=profile_x_medium
4th rowhttps://a0.muscache.com/im/pictures/user/67b13cea-8c11-49c0-a08d-7f42c330676e.jpg?aki_policy=profile_x_medium
5th rowhttps://a0.muscache.com/im/pictures/user/315ddc81-bea3-4bf0-8fc7-be197a6541ff.jpg?aki_policy=profile_x_medium
ValueCountFrequency (%)
https://a0.muscache.com/defaults/user_pic-225x225.png?v=3 533
 
2.7%
https://a0.muscache.com/im/pictures/user/c1738b63-30f9-40ff-b53a-4ea0640c3beb.jpg?aki_policy=profile_x_medium 159
 
0.8%
https://a0.muscache.com/im/pictures/user/cb326f23-ef4a-4fee-a71a-8584e5af6815.jpg?aki_policy=profile_x_medium 116
 
0.6%
https://a0.muscache.com/im/pictures/user/f855bc7e-c7f4-4cb6-8cb5-d81d2a58336a.jpg?aki_policy=profile_x_medium 77
 
0.4%
https://a0.muscache.com/im/pictures/user/da4143cd-6939-4e65-b412-4f7990518552.jpg?aki_policy=profile_x_medium 67
 
0.3%
https://a0.muscache.com/im/pictures/user/69eb44d3-3b36-4743-9d01-92bad2fb4a83.jpg?aki_policy=profile_x_medium 59
 
0.3%
https://a0.muscache.com/im/pictures/user/da4efd54-caaa-4203-970a-8aa4bae02f61.jpg?aki_policy=profile_x_medium 58
 
0.3%
https://a0.muscache.com/im/pictures/user/user-13580277/original/51401555-7a4b-4067-89e3-3cb405420c16.jpeg?aki_policy=profile_x_medium 49
 
0.3%
https://a0.muscache.com/im/pictures/user/57b501f7-d78e-4707-af93-dfacb7536fed.jpg?aki_policy=profile_x_medium 46
 
0.2%
https://a0.muscache.com/im/users/532498/profile_pic/1369411907/original.jpg?aki_policy=profile_x_medium 46
 
0.2%
Other values (11987) 18385
93.8%
2023-10-24T21:48:47.525109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 133562
 
6.2%
c 128972
 
6.0%
i 125371
 
5.8%
/ 124976
 
5.8%
a 99892
 
4.6%
p 98891
 
4.6%
m 96366
 
4.5%
s 80622
 
3.7%
u 75847
 
3.5%
- 71055
 
3.3%
Other values (31) 1118733
51.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1355671
62.9%
Decimal Number 423070
 
19.6%
Other Punctuation 222941
 
10.3%
Dash Punctuation 71055
 
3.3%
Connector Punctuation 59707
 
2.8%
Math Symbol 19593
 
0.9%
Uppercase Letter 2250
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 133562
 
9.9%
c 128972
 
9.5%
i 125371
 
9.2%
a 99892
 
7.4%
p 98891
 
7.3%
m 96366
 
7.1%
s 80622
 
5.9%
u 75847
 
5.6%
r 64205
 
4.7%
o 63951
 
4.7%
Other values (13) 387992
28.6%
Decimal Number
ValueCountFrequency (%)
0 56183
13.3%
4 55644
13.2%
9 40507
9.6%
8 40077
9.5%
3 39832
9.4%
1 39488
9.3%
2 39330
9.3%
5 39014
9.2%
7 36570
8.6%
6 36425
8.6%
Other Punctuation
ValueCountFrequency (%)
/ 124976
56.1%
. 58779
26.4%
? 19593
 
8.8%
: 19593
 
8.8%
Dash Punctuation
ValueCountFrequency (%)
- 71055
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 59707
100.0%
Math Symbol
ValueCountFrequency (%)
= 19593
100.0%
Uppercase Letter
ValueCountFrequency (%)
U 2250
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1357921
63.0%
Common 796366
37.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 133562
 
9.8%
c 128972
 
9.5%
i 125371
 
9.2%
a 99892
 
7.4%
p 98891
 
7.3%
m 96366
 
7.1%
s 80622
 
5.9%
u 75847
 
5.6%
r 64205
 
4.7%
o 63951
 
4.7%
Other values (14) 390242
28.7%
Common
ValueCountFrequency (%)
/ 124976
15.7%
- 71055
 
8.9%
_ 59707
 
7.5%
. 58779
 
7.4%
0 56183
 
7.1%
4 55644
 
7.0%
9 40507
 
5.1%
8 40077
 
5.0%
3 39832
 
5.0%
1 39488
 
5.0%
Other values (7) 210118
26.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2154287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 133562
 
6.2%
c 128972
 
6.0%
i 125371
 
5.8%
/ 124976
 
5.8%
a 99892
 
4.6%
p 98891
 
4.6%
m 96366
 
4.5%
s 80622
 
3.7%
u 75847
 
3.5%
- 71055
 
3.3%
Other values (31) 1118733
51.9%
Distinct327
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:47.775885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length62
Median length33
Mean length9.6600153
Min length2

Characters and Unicode

Total characters189288
Distinct characters70
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique143 ?
Unique (%)0.7%

Sample

1st rowCopacabana
2nd rowCopacabana
3rd rowCopacabana
4th rowCopacabana
5th rowCopacabana
ValueCountFrequency (%)
copacabana 5054
19.1%
no_info 4131
15.6%
tijuca 1929
 
7.3%
barra 1824
 
6.9%
da 1804
 
6.8%
ipanema 1564
 
5.9%
bandeirantes 781
 
3.0%
dos 779
 
2.9%
recreio 777
 
2.9%
leblon 681
 
2.6%
Other values (382) 7085
26.8%
2023-10-24T21:48:48.283202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 40389
21.3%
o 19870
 
10.5%
n 19553
 
10.3%
i 9811
 
5.2%
e 9633
 
5.1%
c 8825
 
4.7%
r 8457
 
4.5%
p 7372
 
3.9%
6814
 
3.6%
C 6088
 
3.2%
Other values (60) 52476
27.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 158800
83.9%
Uppercase Letter 19522
 
10.3%
Space Separator 6814
 
3.6%
Connector Punctuation 4131
 
2.2%
Dash Punctuation 7
 
< 0.1%
Open Punctuation 4
 
< 0.1%
Close Punctuation 4
 
< 0.1%
Other Punctuation 4
 
< 0.1%
Decimal Number 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 40389
25.4%
o 19870
12.5%
n 19553
12.3%
i 9811
 
6.2%
e 9633
 
6.1%
c 8825
 
5.6%
r 8457
 
5.3%
p 7372
 
4.6%
b 5991
 
3.8%
f 4748
 
3.0%
Other values (27) 24151
15.2%
Uppercase Letter
ValueCountFrequency (%)
C 6088
31.2%
B 3434
17.6%
T 2409
 
12.3%
I 1780
 
9.1%
L 1436
 
7.4%
R 877
 
4.5%
S 672
 
3.4%
J 661
 
3.4%
G 495
 
2.5%
V 457
 
2.3%
Other values (16) 1213
 
6.2%
Space Separator
ValueCountFrequency (%)
6814
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4131
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%
Decimal Number
ValueCountFrequency (%)
6 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 178322
94.2%
Common 10966
 
5.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 40389
22.6%
o 19870
11.1%
n 19553
11.0%
i 9811
 
5.5%
e 9633
 
5.4%
c 8825
 
4.9%
r 8457
 
4.7%
p 7372
 
4.1%
C 6088
 
3.4%
b 5991
 
3.4%
Other values (53) 42333
23.7%
Common
ValueCountFrequency (%)
6814
62.1%
_ 4131
37.7%
- 7
 
0.1%
( 4
 
< 0.1%
) 4
 
< 0.1%
. 4
 
< 0.1%
6 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 187833
99.2%
None 1455
 
0.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 40389
21.5%
o 19870
 
10.6%
n 19553
 
10.4%
i 9811
 
5.2%
e 9633
 
5.1%
c 8825
 
4.7%
r 8457
 
4.5%
p 7372
 
3.9%
6814
 
3.6%
C 6088
 
3.2%
Other values (48) 51021
27.2%
None
ValueCountFrequency (%)
á 829
57.0%
ã 225
 
15.5%
ó 188
 
12.9%
â 76
 
5.2%
ç 50
 
3.4%
ú 26
 
1.8%
é 25
 
1.7%
í 19
 
1.3%
ê 11
 
0.8%
Á 4
 
0.3%
Other values (2) 2
 
0.1%

host_listings_count
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size306.2 KiB

host_total_listings_count
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size306.2 KiB

host_verifications
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size306.2 KiB
['email', 'phone']
15491 
['phone']
2607 
['email', 'phone', 'work_email']
 
1388
['phone', 'work_email']
 
64
['email']
 
29
Other values (4)
 
16

Length

Max length34
Median length18
Mean length17.788517
Min length2

Characters and Unicode

Total characters348566
Distinct characters21
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row['email', 'phone']
2nd row['email', 'phone']
3rd row['email', 'phone']
4th row['email', 'phone']
5th row['email', 'phone']

Common Values

ValueCountFrequency (%)
['email', 'phone'] 15491
79.1%
['phone'] 2607
 
13.3%
['email', 'phone', 'work_email'] 1388
 
7.1%
['phone', 'work_email'] 64
 
0.3%
['email'] 29
 
0.1%
[] 11
 
0.1%
['email', 'work_email'] 2
 
< 0.1%
no_info 2
 
< 0.1%
['email', 'phone', 'photographer'] 1
 
< 0.1%

Length

2023-10-24T21:48:48.496132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-24T21:48:48.689186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
phone 19551
51.5%
email 16911
44.6%
work_email 1454
 
3.8%
11
 
< 0.1%
no_info 2
 
< 0.1%
photographer 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
' 75834
21.8%
e 37917
10.9%
o 21011
 
6.0%
[ 19593
 
5.6%
] 19593
 
5.6%
n 19555
 
5.6%
p 19553
 
5.6%
h 19553
 
5.6%
i 18367
 
5.3%
a 18366
 
5.3%
Other values (11) 79224
22.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 195420
56.1%
Other Punctuation 94169
27.0%
Open Punctuation 19593
 
5.6%
Close Punctuation 19593
 
5.6%
Space Separator 18335
 
5.3%
Connector Punctuation 1456
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 37917
19.4%
o 21011
10.8%
n 19555
10.0%
p 19553
10.0%
h 19553
10.0%
i 18367
9.4%
a 18366
9.4%
l 18365
9.4%
m 18365
9.4%
r 1456
 
0.7%
Other values (5) 2912
 
1.5%
Other Punctuation
ValueCountFrequency (%)
' 75834
80.5%
, 18335
 
19.5%
Open Punctuation
ValueCountFrequency (%)
[ 19593
100.0%
Close Punctuation
ValueCountFrequency (%)
] 19593
100.0%
Space Separator
ValueCountFrequency (%)
18335
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1456
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 195420
56.1%
Common 153146
43.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 37917
19.4%
o 21011
10.8%
n 19555
10.0%
p 19553
10.0%
h 19553
10.0%
i 18367
9.4%
a 18366
9.4%
l 18365
9.4%
m 18365
9.4%
r 1456
 
0.7%
Other values (5) 2912
 
1.5%
Common
ValueCountFrequency (%)
' 75834
49.5%
[ 19593
 
12.8%
] 19593
 
12.8%
18335
 
12.0%
, 18335
 
12.0%
_ 1456
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 348566
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 75834
21.8%
e 37917
10.9%
o 21011
 
6.0%
[ 19593
 
5.6%
] 19593
 
5.6%
n 19555
 
5.6%
p 19553
 
5.6%
h 19553
 
5.6%
i 18367
 
5.3%
a 18366
 
5.3%
Other values (11) 79224
22.7%

host_has_profile_pic
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size306.2 KiB
t
19060 
f
 
533
no_info
 
2

Length

Max length7
Median length1
Mean length1.0006124
Min length1

Characters and Unicode

Total characters19607
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowt
2nd rowt
3rd rowt
4th rowt
5th rowt

Common Values

ValueCountFrequency (%)
t 19060
97.3%
f 533
 
2.7%
no_info 2
 
< 0.1%

Length

2023-10-24T21:48:48.903196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-24T21:48:49.062325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
t 19060
97.3%
f 533
 
2.7%
no_info 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
t 19060
97.2%
f 535
 
2.7%
n 4
 
< 0.1%
o 4
 
< 0.1%
_ 2
 
< 0.1%
i 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 19605
> 99.9%
Connector Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 19060
97.2%
f 535
 
2.7%
n 4
 
< 0.1%
o 4
 
< 0.1%
i 2
 
< 0.1%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19605
> 99.9%
Common 2
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 19060
97.2%
f 535
 
2.7%
n 4
 
< 0.1%
o 4
 
< 0.1%
i 2
 
< 0.1%
Common
ValueCountFrequency (%)
_ 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19607
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 19060
97.2%
f 535
 
2.7%
n 4
 
< 0.1%
o 4
 
< 0.1%
_ 2
 
< 0.1%
i 2
 
< 0.1%

host_identity_verified
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size306.2 KiB
t
15770 
f
3823 
no_info
 
2

Length

Max length7
Median length1
Mean length1.0006124
Min length1

Characters and Unicode

Total characters19607
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowt
2nd rowt
3rd rowt
4th rowt
5th rowt

Common Values

ValueCountFrequency (%)
t 15770
80.5%
f 3823
 
19.5%
no_info 2
 
< 0.1%

Length

2023-10-24T21:48:49.232229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-24T21:48:49.393993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
t 15770
80.5%
f 3823
 
19.5%
no_info 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
t 15770
80.4%
f 3825
 
19.5%
n 4
 
< 0.1%
o 4
 
< 0.1%
_ 2
 
< 0.1%
i 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 19605
> 99.9%
Connector Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 15770
80.4%
f 3825
 
19.5%
n 4
 
< 0.1%
o 4
 
< 0.1%
i 2
 
< 0.1%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19605
> 99.9%
Common 2
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 15770
80.4%
f 3825
 
19.5%
n 4
 
< 0.1%
o 4
 
< 0.1%
i 2
 
< 0.1%
Common
ValueCountFrequency (%)
_ 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19607
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 15770
80.4%
f 3825
 
19.5%
n 4
 
< 0.1%
o 4
 
< 0.1%
_ 2
 
< 0.1%
i 2
 
< 0.1%
Distinct198
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:49.606168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length128
Median length7
Mean length16.966114
Min length7

Characters and Unicode

Total characters332451
Distinct characters69
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)0.5%

Sample

1st rowno_info
2nd rowno_info
3rd rowRio de Janeiro, Brazil
4th rowRio de Janeiro, Brazil
5th rowRio de Janeiro, Brazil
ValueCountFrequency (%)
rio 10154
18.5%
de 9904
18.1%
janeiro 9879
18.0%
no_info 9827
17.9%
brazil 9769
17.8%
copacabana 1268
 
2.3%
tijuca 410
 
0.7%
barra 388
 
0.7%
da 383
 
0.7%
ipanema 300
 
0.5%
Other values (168) 2513
 
4.6%
2023-10-24T21:48:50.093779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 42601
12.8%
i 40852
12.3%
35204
10.6%
n 32324
9.7%
a 29177
 
8.8%
e 22170
 
6.7%
r 21659
 
6.5%
, 13812
 
4.2%
d 10794
 
3.2%
B 10559
 
3.2%
Other values (59) 73299
22.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 239129
71.9%
Space Separator 35204
 
10.6%
Uppercase Letter 34437
 
10.4%
Other Punctuation 13820
 
4.2%
Connector Punctuation 9827
 
3.0%
Decimal Number 16
 
< 0.1%
Dash Punctuation 12
 
< 0.1%
Open Punctuation 3
 
< 0.1%
Close Punctuation 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 42601
17.8%
i 40852
17.1%
n 32324
13.5%
a 29177
12.2%
e 22170
9.3%
r 21659
9.1%
d 10794
 
4.5%
l 10174
 
4.3%
f 9975
 
4.2%
z 9775
 
4.1%
Other values (22) 9628
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
B 10559
30.7%
R 10419
30.3%
J 10053
29.2%
C 1544
 
4.5%
T 536
 
1.6%
I 339
 
1.0%
L 306
 
0.9%
S 154
 
0.4%
F 132
 
0.4%
G 128
 
0.4%
Other values (11) 267
 
0.8%
Decimal Number
ValueCountFrequency (%)
8 3
18.8%
3 3
18.8%
2 3
18.8%
7 2
12.5%
5 2
12.5%
1 2
12.5%
0 1
 
6.2%
Other Punctuation
ValueCountFrequency (%)
, 13812
99.9%
/ 4
 
< 0.1%
. 3
 
< 0.1%
: 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
35204
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 9827
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 273566
82.3%
Common 58885
 
17.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 42601
15.6%
i 40852
14.9%
n 32324
11.8%
a 29177
10.7%
e 22170
8.1%
r 21659
7.9%
d 10794
 
3.9%
B 10559
 
3.9%
R 10419
 
3.8%
l 10174
 
3.7%
Other values (43) 42837
15.7%
Common
ValueCountFrequency (%)
35204
59.8%
, 13812
 
23.5%
_ 9827
 
16.7%
- 12
 
< 0.1%
/ 4
 
< 0.1%
( 3
 
< 0.1%
) 3
 
< 0.1%
8 3
 
< 0.1%
3 3
 
< 0.1%
2 3
 
< 0.1%
Other values (6) 11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 332113
99.9%
None 338
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 42601
12.8%
i 40852
12.3%
35204
10.6%
n 32324
9.7%
a 29177
 
8.8%
e 22170
 
6.7%
r 21659
 
6.5%
, 13812
 
4.2%
d 10794
 
3.3%
B 10559
 
3.2%
Other values (49) 72961
22.0%
None
ValueCountFrequency (%)
á 202
59.8%
ó 52
 
15.4%
ã 37
 
10.9%
â 25
 
7.4%
í 7
 
2.1%
ú 6
 
1.8%
ç 4
 
1.2%
É 2
 
0.6%
é 2
 
0.6%
ê 1
 
0.3%
Distinct140
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:50.378087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length10.35555
Min length3

Characters and Unicode

Total characters202917
Distinct characters59
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)0.1%

Sample

1st rowCopacabana
2nd rowCopacabana
3rd rowCopacabana
4th rowCopacabana
5th rowCopacabana
ValueCountFrequency (%)
copacabana 6229
22.6%
tijuca 2116
 
7.7%
barra 2003
 
7.3%
da 1974
 
7.2%
ipanema 1912
 
6.9%
jacarepaguá 1066
 
3.9%
recreio 1027
 
3.7%
dos 1027
 
3.7%
bandeirantes 1027
 
3.7%
leblon 926
 
3.4%
Other values (166) 8268
30.0%
2023-10-24T21:48:50.876943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 50650
25.0%
o 14811
 
7.3%
n 14255
 
7.0%
e 13162
 
6.5%
c 11060
 
5.5%
r 10977
 
5.4%
p 9347
 
4.6%
7980
 
3.9%
C 7864
 
3.9%
b 7478
 
3.7%
Other values (49) 55333
27.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 170392
84.0%
Uppercase Letter 24399
 
12.0%
Space Separator 7980
 
3.9%
Close Punctuation 73
 
< 0.1%
Open Punctuation 73
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 50650
29.7%
o 14811
 
8.7%
n 14255
 
8.4%
e 13162
 
7.7%
c 11060
 
6.5%
r 10977
 
6.4%
p 9347
 
5.5%
b 7478
 
4.4%
i 6628
 
3.9%
d 4942
 
2.9%
Other values (21) 27082
15.9%
Uppercase Letter
ValueCountFrequency (%)
C 7864
32.2%
B 4067
16.7%
T 2868
 
11.8%
I 2095
 
8.6%
L 1786
 
7.3%
J 1270
 
5.2%
R 1122
 
4.6%
S 916
 
3.8%
G 651
 
2.7%
F 496
 
2.0%
Other values (15) 1264
 
5.2%
Space Separator
ValueCountFrequency (%)
7980
100.0%
Close Punctuation
ValueCountFrequency (%)
) 73
100.0%
Open Punctuation
ValueCountFrequency (%)
( 73
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 194791
96.0%
Common 8126
 
4.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 50650
26.0%
o 14811
 
7.6%
n 14255
 
7.3%
e 13162
 
6.8%
c 11060
 
5.7%
r 10977
 
5.6%
p 9347
 
4.8%
C 7864
 
4.0%
b 7478
 
3.8%
i 6628
 
3.4%
Other values (46) 48559
24.9%
Common
ValueCountFrequency (%)
7980
98.2%
) 73
 
0.9%
( 73
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 200652
98.9%
None 2265
 
1.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 50650
25.2%
o 14811
 
7.4%
n 14255
 
7.1%
e 13162
 
6.6%
c 11060
 
5.5%
r 10977
 
5.5%
p 9347
 
4.7%
7980
 
4.0%
C 7864
 
3.9%
b 7478
 
3.7%
Other values (39) 53068
26.4%
None
ValueCountFrequency (%)
á 1521
67.2%
ã 265
 
11.7%
ó 196
 
8.7%
â 121
 
5.3%
ú 45
 
2.0%
ç 37
 
1.6%
é 31
 
1.4%
ê 27
 
1.2%
í 21
 
0.9%
Á 1
 
< 0.1%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct13102
Distinct (%)66.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-22.968751
Minimum-23.07305
Maximum-22.74969
Zeros0
Zeros (%)0.0%
Negative19595
Negative (%)100.0%
Memory size306.2 KiB
2023-10-24T21:48:51.088069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-23.07305
5-th percentile-23.01454
Q1-22.984713
median-22.97311
Q3-22.961139
95-th percentile-22.912589
Maximum-22.74969
Range0.32336
Interquartile range (IQR)0.023573948

Descriptive statistics

Standard deviation0.033537666
Coefficient of variation (CV)-0.0014601432
Kurtosis4.6057318
Mean-22.968751
Median Absolute Deviation (MAD)0.0116858
Skewness1.2791753
Sum-450072.68
Variance0.0011247751
MonotonicityNot monotonic
2023-10-24T21:48:51.313736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-22.9808971 21
 
0.1%
-22.9980396 15
 
0.1%
-23.0045865 15
 
0.1%
-22.98456 13
 
0.1%
-22.985369 12
 
0.1%
-22.98251 12
 
0.1%
-23.03054 11
 
0.1%
-22.98253 11
 
0.1%
-22.98405 11
 
0.1%
-22.98519 10
 
0.1%
Other values (13092) 19464
99.3%
ValueCountFrequency (%)
-23.07305 1
< 0.1%
-23.07252 1
< 0.1%
-23.0722 1
< 0.1%
-23.07215 1
< 0.1%
-23.07211 1
< 0.1%
-23.07209845 1
< 0.1%
-23.07201189 1
< 0.1%
-23.07157934 1
< 0.1%
-23.07123395 1
< 0.1%
-23.0712 1
< 0.1%
ValueCountFrequency (%)
-22.74969 1
< 0.1%
-22.75061 1
< 0.1%
-22.75077 1
< 0.1%
-22.75094 1
< 0.1%
-22.75252 1
< 0.1%
-22.75295 1
< 0.1%
-22.75316 1
< 0.1%
-22.7532937 1
< 0.1%
-22.75358 1
< 0.1%
-22.75409 1
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct13908
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-43.2505
Minimum-43.723009
Maximum-43.10505
Zeros0
Zeros (%)0.0%
Negative19595
Negative (%)100.0%
Memory size306.2 KiB
2023-10-24T21:48:51.534080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-43.723009
5-th percentile-43.472843
Q1-43.309633
median-43.19459
Q3-43.185726
95-th percentile-43.174769
Maximum-43.10505
Range0.61795934
Interquartile range (IQR)0.12390629

Descriptive statistics

Standard deviation0.10088438
Coefficient of variation (CV)-0.0023325598
Kurtosis1.1065741
Mean-43.2505
Median Absolute Deviation (MAD)0.016679708
Skewness-1.4468415
Sum-847493.55
Variance0.010177657
MonotonicityNot monotonic
2023-10-24T21:48:51.751978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-43.4224405 21
 
0.1%
-43.4731697 15
 
0.1%
-43.3411208 15
 
0.1%
-43.2568208 15
 
0.1%
-43.187908 13
 
0.1%
-43.19053 12
 
0.1%
-43.19024 12
 
0.1%
-43.1911 12
 
0.1%
-43.1956179 11
 
0.1%
-43.19115 11
 
0.1%
Other values (13898) 19458
99.3%
ValueCountFrequency (%)
-43.72300934 1
< 0.1%
-43.71038 1
< 0.1%
-43.70128586 1
< 0.1%
-43.7012179 1
< 0.1%
-43.70074 1
< 0.1%
-43.69867 1
< 0.1%
-43.6946123 1
< 0.1%
-43.69155 1
< 0.1%
-43.69005613 1
< 0.1%
-43.68999 1
< 0.1%
ValueCountFrequency (%)
-43.10505 1
< 0.1%
-43.10575 1
< 0.1%
-43.105767 1
< 0.1%
-43.10579556 1
< 0.1%
-43.10605 1
< 0.1%
-43.10611 1
< 0.1%
-43.10642 1
< 0.1%
-43.10661682 1
< 0.1%
-43.10763 1
< 0.1%
-43.10784 1
< 0.1%
Distinct71
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:51.934117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length18
Mean length18.796836
Min length4

Characters and Unicode

Total characters368324
Distinct characters34
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)0.1%

Sample

1st rowEntire rental unit
2nd rowEntire rental unit
3rd rowEntire rental unit
4th rowEntire condo
5th rowEntire rental unit
ValueCountFrequency (%)
entire 15494
24.6%
unit 14595
23.2%
rental 14595
23.2%
room 4048
 
6.4%
in 4020
 
6.4%
private 3594
 
5.7%
home 1628
 
2.6%
condo 1351
 
2.1%
serviced 587
 
0.9%
apartment 587
 
0.9%
Other values (37) 2397
 
3.8%
2023-10-24T21:48:52.329678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 51045
13.9%
n 51032
13.9%
43301
11.8%
r 39391
10.7%
e 38658
10.5%
i 38601
10.5%
a 20929
5.7%
l 15521
 
4.2%
E 15503
 
4.2%
u 15190
 
4.1%
Other values (24) 39153
10.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 305425
82.9%
Space Separator 43301
 
11.8%
Uppercase Letter 19597
 
5.3%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 51045
16.7%
n 51032
16.7%
r 39391
12.9%
e 38658
12.7%
i 38601
12.6%
a 20929
6.9%
l 15521
 
5.1%
u 15190
 
5.0%
o 13452
 
4.4%
m 6268
 
2.1%
Other values (13) 15338
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
E 15503
79.1%
P 3594
 
18.3%
S 245
 
1.3%
R 211
 
1.1%
C 21
 
0.1%
T 17
 
0.1%
B 3
 
< 0.1%
F 2
 
< 0.1%
V 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
43301
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 325022
88.2%
Common 43302
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 51045
15.7%
n 51032
15.7%
r 39391
12.1%
e 38658
11.9%
i 38601
11.9%
a 20929
6.4%
l 15521
 
4.8%
E 15503
 
4.8%
u 15190
 
4.7%
o 13452
 
4.1%
Other values (22) 25700
7.9%
Common
ValueCountFrequency (%)
43301
> 99.9%
/ 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 368324
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 51045
13.9%
n 51032
13.9%
43301
11.8%
r 39391
10.7%
e 38658
10.5%
i 38601
10.5%
a 20929
5.7%
l 15521
 
4.2%
E 15503
 
4.2%
u 15190
 
4.1%
Other values (24) 39153
10.6%

room_type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size306.2 KiB
Entire home/apt
15621 
Private room
3717 
Shared room
 
245
Hotel room
 
12

Length

Max length15
Median length15
Mean length14.377851
Min length10

Characters and Unicode

Total characters281734
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEntire home/apt
2nd rowEntire home/apt
3rd rowEntire home/apt
4th rowEntire home/apt
5th rowEntire home/apt

Common Values

ValueCountFrequency (%)
Entire home/apt 15621
79.7%
Private room 3717
 
19.0%
Shared room 245
 
1.3%
Hotel room 12
 
0.1%

Length

2023-10-24T21:48:52.761308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-24T21:48:52.928537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
entire 15621
39.9%
home/apt 15621
39.9%
room 3974
 
10.1%
private 3717
 
9.5%
shared 245
 
0.6%
hotel 12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 35216
12.5%
t 34971
12.4%
o 23581
8.4%
r 23557
8.4%
m 19595
 
7.0%
19595
 
7.0%
a 19583
 
7.0%
i 19338
 
6.9%
h 15866
 
5.6%
p 15621
 
5.5%
Other values (9) 54811
19.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 226923
80.5%
Space Separator 19595
 
7.0%
Uppercase Letter 19595
 
7.0%
Other Punctuation 15621
 
5.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 35216
15.5%
t 34971
15.4%
o 23581
10.4%
r 23557
10.4%
m 19595
8.6%
a 19583
8.6%
i 19338
8.5%
h 15866
7.0%
p 15621
6.9%
n 15621
6.9%
Other values (3) 3974
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
E 15621
79.7%
P 3717
 
19.0%
S 245
 
1.3%
H 12
 
0.1%
Space Separator
ValueCountFrequency (%)
19595
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 15621
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 246518
87.5%
Common 35216
 
12.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 35216
14.3%
t 34971
14.2%
o 23581
9.6%
r 23557
9.6%
m 19595
7.9%
a 19583
7.9%
i 19338
7.8%
h 15866
6.4%
p 15621
6.3%
E 15621
6.3%
Other values (7) 23569
9.6%
Common
ValueCountFrequency (%)
19595
55.6%
/ 15621
44.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 281734
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 35216
12.5%
t 34971
12.4%
o 23581
8.4%
r 23557
8.4%
m 19595
 
7.0%
19595
 
7.0%
a 19583
 
7.0%
i 19338
 
6.9%
h 15866
 
5.6%
p 15621
 
5.5%
Other values (9) 54811
19.5%

accommodates
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9781067
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:53.088698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median4
Q35
95-th percentile8
Maximum16
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1859509
Coefficient of variation (CV)0.54949531
Kurtosis5.633458
Mean3.9781067
Median Absolute Deviation (MAD)2
Skewness1.7922921
Sum77951
Variance4.7783815
MonotonicityNot monotonic
2023-10-24T21:48:53.265715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
4 5846
29.8%
2 5230
26.7%
6 2378
12.1%
3 2267
 
11.6%
5 1494
 
7.6%
1 763
 
3.9%
8 593
 
3.0%
7 383
 
2.0%
10 247
 
1.3%
12 102
 
0.5%
Other values (6) 292
 
1.5%
ValueCountFrequency (%)
1 763
 
3.9%
2 5230
26.7%
3 2267
 
11.6%
4 5846
29.8%
5 1494
 
7.6%
6 2378
12.1%
7 383
 
2.0%
8 593
 
3.0%
9 98
 
0.5%
10 247
 
1.3%
ValueCountFrequency (%)
16 80
 
0.4%
15 32
 
0.2%
14 30
 
0.2%
13 26
 
0.1%
12 102
 
0.5%
11 26
 
0.1%
10 247
1.3%
9 98
 
0.5%
8 593
3.0%
7 383
2.0%

bathrooms_text
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct40
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size306.2 KiB
1 bath
9095 
2 baths
3988 
1 shared bath
1265 
1 private bath
1159 
1.5 baths
1027 
Other values (35)
3061 

Length

Max length17
Median length16
Mean length7.8021944
Min length6

Characters and Unicode

Total characters152884
Distinct characters32
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row1 bath
2nd rowno_info
3rd row1 bath
4th row1 bath
5th row1 bath

Common Values

ValueCountFrequency (%)
1 bath 9095
46.4%
2 baths 3988
20.4%
1 shared bath 1265
 
6.5%
1 private bath 1159
 
5.9%
1.5 baths 1027
 
5.2%
3 baths 954
 
4.9%
2.5 baths 688
 
3.5%
2 shared baths 242
 
1.2%
3.5 baths 227
 
1.2%
1.5 shared baths 226
 
1.2%
Other values (30) 724
 
3.7%

Length

2023-10-24T21:48:53.470677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 11519
27.3%
bath 11519
27.3%
baths 8024
19.0%
2 4230
 
10.0%
shared 1880
 
4.5%
1.5 1253
 
3.0%
private 1160
 
2.8%
3 980
 
2.3%
2.5 728
 
1.7%
4 245
 
0.6%
Other values (17) 640
 
1.5%

Most occurring characters

ValueCountFrequency (%)
a 22661
14.8%
22583
14.8%
h 21475
14.0%
t 20742
13.6%
b 19582
12.8%
1 12783
8.4%
s 9892
6.5%
2 4958
 
3.2%
r 3040
 
2.0%
e 3040
 
2.0%
Other values (22) 12128
7.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 105947
69.3%
Space Separator 22583
 
14.8%
Decimal Number 21907
 
14.3%
Other Punctuation 2356
 
1.5%
Dash Punctuation 39
 
< 0.1%
Uppercase Letter 39
 
< 0.1%
Connector Punctuation 13
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 22661
21.4%
h 21475
20.3%
t 20742
19.6%
b 19582
18.5%
s 9892
9.3%
r 3040
 
2.9%
e 3040
 
2.9%
d 1880
 
1.8%
i 1173
 
1.1%
v 1160
 
1.1%
Other values (5) 1302
 
1.2%
Decimal Number
ValueCountFrequency (%)
1 12783
58.4%
2 4958
 
22.6%
5 2471
 
11.3%
3 1215
 
5.5%
4 332
 
1.5%
6 61
 
0.3%
0 44
 
0.2%
7 33
 
0.2%
8 9
 
< 0.1%
9 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
H 26
66.7%
S 12
30.8%
P 1
 
2.6%
Space Separator
ValueCountFrequency (%)
22583
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2356
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 39
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 105986
69.3%
Common 46898
30.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 22661
21.4%
h 21475
20.3%
t 20742
19.6%
b 19582
18.5%
s 9892
9.3%
r 3040
 
2.9%
e 3040
 
2.9%
d 1880
 
1.8%
i 1173
 
1.1%
v 1160
 
1.1%
Other values (8) 1341
 
1.3%
Common
ValueCountFrequency (%)
22583
48.2%
1 12783
27.3%
2 4958
 
10.6%
5 2471
 
5.3%
. 2356
 
5.0%
3 1215
 
2.6%
4 332
 
0.7%
6 61
 
0.1%
0 44
 
0.1%
- 39
 
0.1%
Other values (4) 56
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 152884
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 22661
14.8%
22583
14.8%
h 21475
14.0%
t 20742
13.6%
b 19582
12.8%
1 12783
8.4%
s 9892
6.5%
2 4958
 
3.2%
r 3040
 
2.0%
e 3040
 
2.0%
Other values (22) 12128
7.9%

bedrooms
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size306.2 KiB

beds
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size306.2 KiB
Distinct18174
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:53.762885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2057
Median length1087
Mean length440.13907
Min length2

Characters and Unicode

Total characters8624525
Distinct characters82
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17691 ?
Unique (%)90.3%

Sample

1st row["Hangers", "Body soap", "Elevator", "Bed linens", "Microwave", "Wifi", "Dishes and silverware", "Hair dryer", "TV", "Iron", "Dining table", "Ceiling fan", "Essentials", "Public or shared beach access \u2013 Beachfront", "Refrigerator", "Coffee maker: drip coffee maker", "Gas stove", "Hot water", "Extra pillows and blankets", "Kitchen", "Air conditioning"]
2nd row["TV", "Kitchen", "Wifi", "Elevator", "Air conditioning"]
3rd row["Clothing storage: wardrobe", "Public or shared beach access", "Hangers", "Esmaltec gas stove", "Elevator", "Cooking basics", "32\" HDTV", "Room-darkening shades", "Bed linens", "Microwave", "Free washer \u2013 In unit", "Drying rack for clothing", "Dishes and silverware", "Cleaning products", "Courtyard view", "Coffee maker", "Iron", "Laundromat nearby", "Dining table", "Ceiling fan", "Blender", "Essentials", "Hot water kettle", "Refrigerator", "Host greets you", "EV charger", "Mountain view", "Hot water", "Oven", "Kitchen", "Window AC unit", "Wifi \u2013 14 Mbps"]
4th row["Patio or balcony", "Hangers", "Paid parking off premises", "Elevator", "Cooking basics", "Private entrance", "Bed linens", "Microwave", "Wifi", "Dishes and silverware", "Hair dryer", "Self check-in", "Iron", "Building staff", "Essentials", "TV with standard cable", "Refrigerator", "Stove", "Smoking allowed", "Bathtub", "Hot water", "Oven", "Air conditioning", "Kitchen", "Luggage dropoff allowed", "Coffee maker"]
5th row["Clothing storage: wardrobe", "Dedicated workspace", "Public or shared beach access", "Hangers", "Elevator", "Cooking basics", "Room-darkening shades", "Bed linens", "Single level home", "Microwave", "Wifi", "Drying rack for clothing", "Dishes and silverware", "Hair dryer", "Iron", "Laundromat nearby", "Paid street parking off premises", "Dining table", "Ceiling fan", "Books and reading material", "Blender", "Essentials", "Hammock", "TV with standard cable", "Refrigerator", "Stove", "Host greets you", "Hot water", "Oven", "Kitchen", "Window AC unit", "Coffee maker"]
ValueCountFrequency (%)
and 22609
 
2.2%
allowed 20978
 
2.0%
wifi 19038
 
1.8%
kitchen 18120
 
1.7%
coffee 16187
 
1.6%
parking 16092
 
1.5%
free 15627
 
1.5%
hot 15547
 
1.5%
essentials 14417
 
1.4%
water 14210
 
1.4%
Other values (1864) 869759
83.4%
2023-10-24T21:48:54.304496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1023940
 
11.9%
" 955721
 
11.1%
e 694048
 
8.0%
a 492949
 
5.7%
r 485003
 
5.6%
i 475695
 
5.5%
, 463840
 
5.4%
o 416513
 
4.8%
n 404722
 
4.7%
s 383265
 
4.4%
Other values (72) 2828829
32.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5496995
63.7%
Other Punctuation 1447109
 
16.8%
Space Separator 1023940
 
11.9%
Uppercase Letter 536927
 
6.2%
Decimal Number 61525
 
0.7%
Close Punctuation 19626
 
0.2%
Open Punctuation 19625
 
0.2%
Dash Punctuation 18261
 
0.2%
Math Symbol 517
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 694048
12.6%
a 492949
 
9.0%
r 485003
 
8.8%
i 475695
 
8.7%
o 416513
 
7.6%
n 404722
 
7.4%
s 383265
 
7.0%
t 340326
 
6.2%
l 241258
 
4.4%
d 215709
 
3.9%
Other values (16) 1347507
24.5%
Uppercase Letter
ValueCountFrequency (%)
C 56678
 
10.6%
B 48785
 
9.1%
H 45153
 
8.4%
D 41474
 
7.7%
S 36649
 
6.8%
E 36502
 
6.8%
W 35803
 
6.7%
P 33655
 
6.3%
F 28433
 
5.3%
T 22443
 
4.2%
Other values (16) 151352
28.2%
Other Punctuation
ValueCountFrequency (%)
" 955721
66.0%
, 463840
32.1%
\ 15793
 
1.1%
: 11041
 
0.8%
/ 525
 
< 0.1%
. 131
 
< 0.1%
' 32
 
< 0.1%
! 12
 
< 0.1%
& 8
 
< 0.1%
% 3
 
< 0.1%
Other values (2) 3
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 14794
24.0%
2 14298
23.2%
1 13234
21.5%
3 11955
19.4%
9 2137
 
3.5%
4 2108
 
3.4%
5 1803
 
2.9%
6 501
 
0.8%
8 352
 
0.6%
7 343
 
0.6%
Open Punctuation
ValueCountFrequency (%)
[ 19595
99.8%
( 30
 
0.2%
Close Punctuation
ValueCountFrequency (%)
] 19595
99.8%
) 31
 
0.2%
Math Symbol
ValueCountFrequency (%)
+ 512
99.0%
| 5
 
1.0%
Space Separator
ValueCountFrequency (%)
1023940
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18261
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6033922
70.0%
Common 2590603
30.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 694048
 
11.5%
a 492949
 
8.2%
r 485003
 
8.0%
i 475695
 
7.9%
o 416513
 
6.9%
n 404722
 
6.7%
s 383265
 
6.4%
t 340326
 
5.6%
l 241258
 
4.0%
d 215709
 
3.6%
Other values (42) 1884434
31.2%
Common
ValueCountFrequency (%)
1023940
39.5%
" 955721
36.9%
, 463840
17.9%
[ 19595
 
0.8%
] 19595
 
0.8%
- 18261
 
0.7%
\ 15793
 
0.6%
0 14794
 
0.6%
2 14298
 
0.6%
1 13234
 
0.5%
Other values (20) 31532
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8624525
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1023940
 
11.9%
" 955721
 
11.1%
e 694048
 
8.0%
a 492949
 
5.7%
r 485003
 
5.6%
i 475695
 
5.5%
, 463840
 
5.4%
o 416513
 
4.8%
n 404722
 
4.7%
s 383265
 
4.4%
Other values (72) 2828829
32.8%

price
Real number (ℝ)

HIGH CORRELATION 

Distinct1433
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean515.95815
Minimum75
Maximum4277
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:54.521036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum75
5-th percentile110
Q1199
median318
Q3588
95-th percentile1600
Maximum4277
Range4202
Interquartile range (IQR)389

Descriptive statistics

Standard deviation571.47039
Coefficient of variation (CV)1.1075906
Kurtosis11.565287
Mean515.95815
Median Absolute Deviation (MAD)152
Skewness3.0654956
Sum10110200
Variance326578.4
MonotonicityNot monotonic
2023-10-24T21:48:54.734327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 501
 
2.6%
300 470
 
2.4%
250 465
 
2.4%
500 413
 
2.1%
400 396
 
2.0%
350 395
 
2.0%
150 382
 
1.9%
1000 273
 
1.4%
450 271
 
1.4%
180 268
 
1.4%
Other values (1423) 15761
80.4%
ValueCountFrequency (%)
75 43
0.2%
76 21
 
0.1%
77 8
 
< 0.1%
78 12
 
0.1%
79 16
 
0.1%
80 66
0.3%
81 16
 
0.1%
82 7
 
< 0.1%
83 15
 
0.1%
84 13
 
0.1%
ValueCountFrequency (%)
4277 1
< 0.1%
4271 1
< 0.1%
4212 1
< 0.1%
4210 1
< 0.1%
4200 1
< 0.1%
4178 1
< 0.1%
4168 2
< 0.1%
4162 1
< 0.1%
4155 1
< 0.1%
4143 1
< 0.1%

minimum_nights
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3792804
Minimum1
Maximum730
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:54.939571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile10
Maximum730
Range729
Interquartile range (IQR)1

Descriptive statistics

Standard deviation16.985955
Coefficient of variation (CV)3.8787091
Kurtosis474.58517
Mean4.3792804
Median Absolute Deviation (MAD)1
Skewness19.488288
Sum85812
Variance288.52267
MonotonicityNot monotonic
2023-10-24T21:48:55.164211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 6028
30.8%
1 4514
23.0%
3 4326
22.1%
4 1456
 
7.4%
5 1305
 
6.7%
7 581
 
3.0%
10 305
 
1.6%
6 231
 
1.2%
15 220
 
1.1%
30 163
 
0.8%
Other values (47) 466
 
2.4%
ValueCountFrequency (%)
1 4514
23.0%
2 6028
30.8%
3 4326
22.1%
4 1456
 
7.4%
5 1305
 
6.7%
6 231
 
1.2%
7 581
 
3.0%
8 38
 
0.2%
9 11
 
0.1%
10 305
 
1.6%
ValueCountFrequency (%)
730 1
 
< 0.1%
365 23
0.1%
362 1
 
< 0.1%
360 3
 
< 0.1%
300 4
 
< 0.1%
200 1
 
< 0.1%
188 1
 
< 0.1%
180 9
 
< 0.1%
170 1
 
< 0.1%
150 3
 
< 0.1%

maximum_nights
Real number (ℝ)

HIGH CORRELATION 

Distinct181
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean191.94075
Minimum8
Maximum1124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:55.400533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile14
Q130
median90
Q3365
95-th percentile365
Maximum1124
Range1116
Interquartile range (IQR)335

Descriptive statistics

Standard deviation176.25823
Coefficient of variation (CV)0.91829501
Kurtosis2.1389543
Mean191.94075
Median Absolute Deviation (MAD)75
Skewness1.0417892
Sum3761079
Variance31066.965
MonotonicityNot monotonic
2023-10-24T21:48:55.615584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
365 7627
38.9%
90 2727
 
13.9%
30 2686
 
13.7%
60 952
 
4.9%
15 826
 
4.2%
10 680
 
3.5%
20 449
 
2.3%
89 372
 
1.9%
180 346
 
1.8%
28 328
 
1.7%
Other values (171) 2602
 
13.3%
ValueCountFrequency (%)
8 89
 
0.5%
9 30
 
0.2%
10 680
3.5%
11 12
 
0.1%
12 81
 
0.4%
13 10
 
0.1%
14 124
 
0.6%
15 826
4.2%
16 20
 
0.1%
17 11
 
0.1%
ValueCountFrequency (%)
1124 26
0.1%
1123 3
 
< 0.1%
1122 1
 
< 0.1%
1121 1
 
< 0.1%
1120 4
 
< 0.1%
1110 1
 
< 0.1%
1109 1
 
< 0.1%
1100 3
 
< 0.1%
1097 1
 
< 0.1%
1095 6
 
< 0.1%

minimum_minimum_nights
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct56
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1375351
Minimum1
Maximum730
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:55.822183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile10
Maximum730
Range729
Interquartile range (IQR)2

Descriptive statistics

Standard deviation16.253203
Coefficient of variation (CV)3.9282332
Kurtosis524.2496
Mean4.1375351
Median Absolute Deviation (MAD)1
Skewness20.345416
Sum81075
Variance264.1666
MonotonicityNot monotonic
2023-10-24T21:48:56.050725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 6174
31.5%
1 5016
25.6%
3 4110
21.0%
4 1338
 
6.8%
5 1170
 
6.0%
7 537
 
2.7%
10 270
 
1.4%
15 212
 
1.1%
6 205
 
1.0%
30 153
 
0.8%
Other values (46) 410
 
2.1%
ValueCountFrequency (%)
1 5016
25.6%
2 6174
31.5%
3 4110
21.0%
4 1338
 
6.8%
5 1170
 
6.0%
6 205
 
1.0%
7 537
 
2.7%
8 30
 
0.2%
9 11
 
0.1%
10 270
 
1.4%
ValueCountFrequency (%)
730 1
 
< 0.1%
365 20
0.1%
360 3
 
< 0.1%
300 5
 
< 0.1%
200 1
 
< 0.1%
188 1
 
< 0.1%
180 9
< 0.1%
170 1
 
< 0.1%
150 3
 
< 0.1%
120 8
 
< 0.1%

maximum_minimum_nights
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1782087
Minimum1
Maximum730
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:56.267379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile10
Maximum730
Range729
Interquartile range (IQR)3

Descriptive statistics

Standard deviation16.866941
Coefficient of variation (CV)3.2572927
Kurtosis469.67023
Mean5.1782087
Median Absolute Deviation (MAD)2
Skewness19.194194
Sum101467
Variance284.49371
MonotonicityNot monotonic
2023-10-24T21:48:56.490405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 3965
20.2%
1 3538
18.1%
3 3483
17.8%
5 3291
16.8%
4 1908
9.7%
7 1500
 
7.7%
6 502
 
2.6%
10 412
 
2.1%
15 236
 
1.2%
30 184
 
0.9%
Other values (51) 576
 
2.9%
ValueCountFrequency (%)
1 3538
18.1%
2 3965
20.2%
3 3483
17.8%
4 1908
9.7%
5 3291
16.8%
6 502
 
2.6%
7 1500
 
7.7%
8 89
 
0.5%
9 32
 
0.2%
10 412
 
2.1%
ValueCountFrequency (%)
730 1
 
< 0.1%
365 22
0.1%
360 3
 
< 0.1%
300 5
 
< 0.1%
200 2
 
< 0.1%
188 1
 
< 0.1%
180 9
< 0.1%
170 1
 
< 0.1%
150 3
 
< 0.1%
140 1
 
< 0.1%

minimum_maximum_nights
Real number (ℝ)

HIGH CORRELATION 

Distinct171
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean380.45583
Minimum1
Maximum1125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:56.715994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15
Q140
median365
Q3365
95-th percentile1125
Maximum1125
Range1124
Interquartile range (IQR)325

Descriptive statistics

Standard deviation402.59355
Coefficient of variation (CV)1.0581874
Kurtosis-0.43115414
Mean380.45583
Median Absolute Deviation (MAD)305
Skewness1.0268103
Sum7455032
Variance162081.57
MonotonicityNot monotonic
2023-10-24T21:48:56.931137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
365 6384
32.6%
1125 3862
19.7%
90 2170
 
11.1%
30 2076
 
10.6%
60 700
 
3.6%
15 679
 
3.5%
10 583
 
3.0%
20 359
 
1.8%
180 243
 
1.2%
89 231
 
1.2%
Other values (161) 2308
 
11.8%
ValueCountFrequency (%)
1 2
 
< 0.1%
2 4
 
< 0.1%
3 3
 
< 0.1%
4 11
 
0.1%
5 14
 
0.1%
6 7
 
< 0.1%
7 18
 
0.1%
8 76
 
0.4%
9 26
 
0.1%
10 583
3.0%
ValueCountFrequency (%)
1125 3862
19.7%
1124 16
 
0.1%
1123 3
 
< 0.1%
1122 1
 
< 0.1%
1121 1
 
< 0.1%
1120 4
 
< 0.1%
1110 1
 
< 0.1%
1109 1
 
< 0.1%
1097 1
 
< 0.1%
1095 5
 
< 0.1%

maximum_maximum_nights
Real number (ℝ)

HIGH CORRELATION 

Distinct164
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean405.70472
Minimum8
Maximum1125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:57.141584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile15
Q160
median365
Q3365
95-th percentile1125
Maximum1125
Range1117
Interquartile range (IQR)305

Descriptive statistics

Standard deviation414.41068
Coefficient of variation (CV)1.0214589
Kurtosis-0.72266826
Mean405.70472
Median Absolute Deviation (MAD)305
Skewness0.90582933
Sum7949784
Variance171736.21
MonotonicityNot monotonic
2023-10-24T21:48:57.361263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
365 6410
32.7%
1125 4316
22.0%
30 1995
 
10.2%
90 1951
 
10.0%
60 685
 
3.5%
15 651
 
3.3%
10 551
 
2.8%
20 350
 
1.8%
180 242
 
1.2%
89 241
 
1.2%
Other values (154) 2203
 
11.2%
ValueCountFrequency (%)
8 74
 
0.4%
9 24
 
0.1%
10 551
2.8%
11 8
 
< 0.1%
12 67
 
0.3%
13 8
 
< 0.1%
14 100
 
0.5%
15 651
3.3%
16 13
 
0.1%
17 10
 
0.1%
ValueCountFrequency (%)
1125 4316
22.0%
1124 17
 
0.1%
1123 3
 
< 0.1%
1122 1
 
< 0.1%
1121 1
 
< 0.1%
1120 4
 
< 0.1%
1110 1
 
< 0.1%
1109 1
 
< 0.1%
1100 1
 
< 0.1%
1097 1
 
< 0.1%

minimum_nights_avg_ntm
Real number (ℝ)

HIGH CORRELATION 

Distinct194
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3919061
Minimum1
Maximum730
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:57.570222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2.2
Q33.4
95-th percentile10
Maximum730
Range729
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation16.459663
Coefficient of variation (CV)3.7477266
Kurtosis505.95798
Mean4.3919061
Median Absolute Deviation (MAD)0.9
Skewness19.98829
Sum86059.4
Variance270.92052
MonotonicityNot monotonic
2023-10-24T21:48:57.788868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 4149
21.2%
1 3685
18.8%
3 3184
16.2%
5 1168
 
6.0%
4 1151
 
5.9%
2.1 784
 
4.0%
7 531
 
2.7%
3.1 529
 
2.7%
2.2 520
 
2.7%
3.2 316
 
1.6%
Other values (184) 3578
18.3%
ValueCountFrequency (%)
1 3685
18.8%
1.1 223
 
1.1%
1.2 123
 
0.6%
1.3 120
 
0.6%
1.4 101
 
0.5%
1.5 42
 
0.2%
1.6 50
 
0.3%
1.7 20
 
0.1%
1.8 15
 
0.1%
1.9 30
 
0.2%
ValueCountFrequency (%)
730 1
 
< 0.1%
365 20
0.1%
360 3
 
< 0.1%
340.3 1
 
< 0.1%
300 5
 
< 0.1%
200 1
 
< 0.1%
188 1
 
< 0.1%
180 9
< 0.1%
170 1
 
< 0.1%
150 3
 
< 0.1%

maximum_nights_avg_ntm
Real number (ℝ)

HIGH CORRELATION 

Distinct513
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean395.7708
Minimum8
Maximum1125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:58.011610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile15
Q160
median365
Q3365
95-th percentile1125
Maximum1125
Range1117
Interquartile range (IQR)305

Descriptive statistics

Standard deviation404.68127
Coefficient of variation (CV)1.0225142
Kurtosis-0.58386145
Mean395.7708
Median Absolute Deviation (MAD)305
Skewness0.95027691
Sum7755128.8
Variance163766.93
MonotonicityNot monotonic
2023-10-24T21:48:58.231170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
365 6337
32.3%
1125 3862
19.7%
30 1976
 
10.1%
90 1916
 
9.8%
60 681
 
3.5%
15 646
 
3.3%
10 550
 
2.8%
20 341
 
1.7%
180 239
 
1.2%
89 224
 
1.1%
Other values (503) 2823
14.4%
ValueCountFrequency (%)
8 74
 
0.4%
9 24
 
0.1%
9.9 1
 
< 0.1%
10 550
2.8%
10.2 1
 
< 0.1%
10.5 1
 
< 0.1%
11 8
 
< 0.1%
11.9 1
 
< 0.1%
12 67
 
0.3%
13 8
 
< 0.1%
ValueCountFrequency (%)
1125 3862
19.7%
1124 16
 
0.1%
1123 3
 
< 0.1%
1122.5 2
 
< 0.1%
1122.4 4
 
< 0.1%
1122 1
 
< 0.1%
1121 1
 
< 0.1%
1120.5 1
 
< 0.1%
1120 6
 
< 0.1%
1119.8 2
 
< 0.1%

has_availability
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size172.2 KiB
True
19095 
False
 
500
ValueCountFrequency (%)
True 19095
97.4%
False 500
 
2.6%
2023-10-24T21:48:58.403367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

availability_30
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.748099
Minimum0
Maximum30
Zeros3821
Zeros (%)19.5%
Negative0
Negative (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:58.795465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median19
Q329
95-th percentile30
Maximum30
Range30
Interquartile range (IQR)25

Descriptive statistics

Standard deviation11.80936
Coefficient of variation (CV)0.70511646
Kurtosis-1.5596591
Mean16.748099
Median Absolute Deviation (MAD)10
Skewness-0.27145406
Sum328179
Variance139.46099
MonotonicityNot monotonic
2023-10-24T21:48:58.982146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 3821
19.5%
30 3311
16.9%
29 2012
 
10.3%
28 1069
 
5.5%
27 773
 
3.9%
23 630
 
3.2%
21 379
 
1.9%
6 359
 
1.8%
7 357
 
1.8%
9 354
 
1.8%
Other values (21) 6530
33.3%
ValueCountFrequency (%)
0 3821
19.5%
1 319
 
1.6%
2 259
 
1.3%
3 262
 
1.3%
4 339
 
1.7%
5 329
 
1.7%
6 359
 
1.8%
7 357
 
1.8%
8 344
 
1.8%
9 354
 
1.8%
ValueCountFrequency (%)
30 3311
16.9%
29 2012
10.3%
28 1069
 
5.5%
27 773
 
3.9%
26 321
 
1.6%
25 328
 
1.7%
24 326
 
1.7%
23 630
 
3.2%
22 337
 
1.7%
21 379
 
1.9%

availability_60
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct61
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.495484
Minimum0
Maximum60
Zeros3149
Zeros (%)16.1%
Negative0
Negative (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:59.184320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112
median39
Q358
95-th percentile60
Maximum60
Range60
Interquartile range (IQR)46

Descriptive statistics

Standard deviation22.844232
Coefficient of variation (CV)0.66223836
Kurtosis-1.4514844
Mean34.495484
Median Absolute Deviation (MAD)20
Skewness-0.33150731
Sum675939
Variance521.85895
MonotonicityNot monotonic
2023-10-24T21:48:59.403510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3149
 
16.1%
60 2781
 
14.2%
59 1583
 
8.1%
58 802
 
4.1%
57 610
 
3.1%
53 564
 
2.9%
55 268
 
1.4%
51 266
 
1.4%
56 255
 
1.3%
52 248
 
1.3%
Other values (51) 9069
46.3%
ValueCountFrequency (%)
0 3149
16.1%
1 196
 
1.0%
2 136
 
0.7%
3 105
 
0.5%
4 138
 
0.7%
5 138
 
0.7%
6 139
 
0.7%
7 154
 
0.8%
8 157
 
0.8%
9 166
 
0.8%
ValueCountFrequency (%)
60 2781
14.2%
59 1583
8.1%
58 802
 
4.1%
57 610
 
3.1%
56 255
 
1.3%
55 268
 
1.4%
54 241
 
1.2%
53 564
 
2.9%
52 248
 
1.3%
51 266
 
1.4%

availability_90
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct91
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.587395
Minimum0
Maximum90
Zeros2694
Zeros (%)13.7%
Negative0
Negative (%)0.0%
Memory size306.2 KiB
2023-10-24T21:48:59.619008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q129
median65
Q388
95-th percentile90
Maximum90
Range90
Interquartile range (IQR)59

Descriptive statistics

Standard deviation33.041334
Coefficient of variation (CV)0.59440336
Kurtosis-1.1791094
Mean55.587395
Median Absolute Deviation (MAD)24
Skewness-0.55234685
Sum1089235
Variance1091.7298
MonotonicityNot monotonic
2023-10-24T21:48:59.839434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2694
 
13.7%
90 2640
 
13.5%
89 1553
 
7.9%
88 767
 
3.9%
87 580
 
3.0%
83 531
 
2.7%
81 240
 
1.2%
84 225
 
1.1%
82 224
 
1.1%
85 222
 
1.1%
Other values (81) 9919
50.6%
ValueCountFrequency (%)
0 2694
13.7%
1 171
 
0.9%
2 89
 
0.5%
3 72
 
0.4%
4 80
 
0.4%
5 57
 
0.3%
6 74
 
0.4%
7 78
 
0.4%
8 75
 
0.4%
9 85
 
0.4%
ValueCountFrequency (%)
90 2640
13.5%
89 1553
7.9%
88 767
 
3.9%
87 580
 
3.0%
86 221
 
1.1%
85 222
 
1.1%
84 225
 
1.1%
83 531
 
2.7%
82 224
 
1.1%
81 240
 
1.2%

availability_365
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct366
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean186.12243
Minimum0
Maximum365
Zeros1752
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size306.2 KiB
2023-10-24T21:49:00.043107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q174
median171
Q3327
95-th percentile365
Maximum365
Range365
Interquartile range (IQR)253

Descriptive statistics

Standard deviation130.24702
Coefficient of variation (CV)0.69979216
Kurtosis-1.4763082
Mean186.12243
Median Absolute Deviation (MAD)118
Skewness0.081918411
Sum3647069
Variance16964.285
MonotonicityNot monotonic
2023-10-24T21:49:00.264347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1752
 
8.9%
365 1504
 
7.7%
364 580
 
3.0%
89 408
 
2.1%
90 301
 
1.5%
363 290
 
1.5%
358 256
 
1.3%
362 244
 
1.2%
88 214
 
1.1%
87 159
 
0.8%
Other values (356) 13887
70.9%
ValueCountFrequency (%)
0 1752
8.9%
1 70
 
0.4%
2 38
 
0.2%
3 32
 
0.2%
4 53
 
0.3%
5 24
 
0.1%
6 31
 
0.2%
7 39
 
0.2%
8 45
 
0.2%
9 46
 
0.2%
ValueCountFrequency (%)
365 1504
7.7%
364 580
 
3.0%
363 290
 
1.5%
362 244
 
1.2%
361 131
 
0.7%
360 105
 
0.5%
359 108
 
0.6%
358 256
 
1.3%
357 115
 
0.6%
356 99
 
0.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size306.2 KiB
Minimum2023-09-22 00:00:00
Maximum2023-09-23 00:00:00
2023-10-24T21:49:00.442890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-24T21:49:00.584772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

number_of_reviews
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct313
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.94075
Minimum0
Maximum618
Zeros5291
Zeros (%)27.0%
Negative0
Negative (%)0.0%
Memory size306.2 KiB
2023-10-24T21:49:00.762466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q317
95-th percentile85
Maximum618
Range618
Interquartile range (IQR)17

Descriptive statistics

Standard deviation38.825275
Coefficient of variation (CV)2.1640831
Kurtosis37.084487
Mean17.94075
Median Absolute Deviation (MAD)4
Skewness4.9328695
Sum351549
Variance1507.402
MonotonicityNot monotonic
2023-10-24T21:49:00.988844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5291
27.0%
1 2151
 
11.0%
2 1375
 
7.0%
3 949
 
4.8%
4 750
 
3.8%
5 598
 
3.1%
6 516
 
2.6%
7 405
 
2.1%
8 395
 
2.0%
9 367
 
1.9%
Other values (303) 6798
34.7%
ValueCountFrequency (%)
0 5291
27.0%
1 2151
11.0%
2 1375
 
7.0%
3 949
 
4.8%
4 750
 
3.8%
5 598
 
3.1%
6 516
 
2.6%
7 405
 
2.1%
8 395
 
2.0%
9 367
 
1.9%
ValueCountFrequency (%)
618 1
< 0.1%
611 1
< 0.1%
577 1
< 0.1%
542 1
< 0.1%
540 1
< 0.1%
517 1
< 0.1%
510 1
< 0.1%
480 1
< 0.1%
471 1
< 0.1%
448 1
< 0.1%

number_of_reviews_ltm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct88
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8941567
Minimum0
Maximum133
Zeros7356
Zeros (%)37.5%
Negative0
Negative (%)0.0%
Memory size306.2 KiB
2023-10-24T21:49:01.235120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q39
95-th percentile31
Maximum133
Range133
Interquartile range (IQR)9

Descriptive statistics

Standard deviation11.140708
Coefficient of variation (CV)1.6159638
Kurtosis8.2913333
Mean6.8941567
Median Absolute Deviation (MAD)2
Skewness2.4890635
Sum135091
Variance124.11537
MonotonicityNot monotonic
2023-10-24T21:49:01.458912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7356
37.5%
1 2231
 
11.4%
2 1349
 
6.9%
3 922
 
4.7%
4 712
 
3.6%
5 544
 
2.8%
6 477
 
2.4%
7 421
 
2.1%
8 412
 
2.1%
9 379
 
1.9%
Other values (78) 4792
24.5%
ValueCountFrequency (%)
0 7356
37.5%
1 2231
 
11.4%
2 1349
 
6.9%
3 922
 
4.7%
4 712
 
3.6%
5 544
 
2.8%
6 477
 
2.4%
7 421
 
2.1%
8 412
 
2.1%
9 379
 
1.9%
ValueCountFrequency (%)
133 1
 
< 0.1%
120 1
 
< 0.1%
100 2
< 0.1%
99 1
 
< 0.1%
88 2
< 0.1%
87 1
 
< 0.1%
85 2
< 0.1%
84 1
 
< 0.1%
82 1
 
< 0.1%
81 3
< 0.1%

number_of_reviews_l30d
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.61306456
Minimum0
Maximum12
Zeros13624
Zeros (%)69.5%
Negative0
Negative (%)0.0%
Memory size306.2 KiB
2023-10-24T21:49:01.643687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum12
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1812875
Coefficient of variation (CV)1.9268567
Kurtosis8.4905257
Mean0.61306456
Median Absolute Deviation (MAD)0
Skewness2.5781778
Sum12013
Variance1.3954402
MonotonicityNot monotonic
2023-10-24T21:49:01.823714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 13624
69.5%
1 2825
 
14.4%
2 1576
 
8.0%
3 847
 
4.3%
4 387
 
2.0%
5 185
 
0.9%
6 86
 
0.4%
7 40
 
0.2%
8 12
 
0.1%
10 5
 
< 0.1%
Other values (3) 8
 
< 0.1%
ValueCountFrequency (%)
0 13624
69.5%
1 2825
 
14.4%
2 1576
 
8.0%
3 847
 
4.3%
4 387
 
2.0%
5 185
 
0.9%
6 86
 
0.4%
7 40
 
0.2%
8 12
 
0.1%
9 5
 
< 0.1%
ValueCountFrequency (%)
12 2
 
< 0.1%
11 1
 
< 0.1%
10 5
 
< 0.1%
9 5
 
< 0.1%
8 12
 
0.1%
7 40
 
0.2%
6 86
 
0.4%
5 185
 
0.9%
4 387
2.0%
3 847
4.3%
Distinct2579
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Memory size306.2 KiB
2023-10-24T21:49:02.058204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.1899464
Min length7

Characters and Unicode

Total characters180077
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1013 ?
Unique (%)5.2%

Sample

1st row2011-11-17
2nd row2011-11-02
3rd row2014-03-03
4th row2010-07-15
5th row2010-06-07
ValueCountFrequency (%)
no_info 5291
27.0%
2023-02-22 280
 
1.4%
2023-01-02 245
 
1.3%
2022-09-05 208
 
1.1%
2022-09-11 206
 
1.1%
2022-09-04 197
 
1.0%
2023-02-21 173
 
0.9%
2022-09-12 173
 
0.9%
2023-09-10 172
 
0.9%
2022-01-02 134
 
0.7%
Other values (2569) 12516
63.9%
2023-10-24T21:49:02.502138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 37866
21.0%
0 34010
18.9%
- 28608
15.9%
1 17008
9.4%
n 10582
 
5.9%
o 10582
 
5.9%
3 8169
 
4.5%
_ 5291
 
2.9%
i 5291
 
2.9%
f 5291
 
2.9%
Other values (6) 17379
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 114432
63.5%
Lowercase Letter 31746
 
17.6%
Dash Punctuation 28608
 
15.9%
Connector Punctuation 5291
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 37866
33.1%
0 34010
29.7%
1 17008
14.9%
3 8169
 
7.1%
9 4305
 
3.8%
6 2853
 
2.5%
8 2758
 
2.4%
7 2580
 
2.3%
4 2569
 
2.2%
5 2314
 
2.0%
Lowercase Letter
ValueCountFrequency (%)
n 10582
33.3%
o 10582
33.3%
i 5291
16.7%
f 5291
16.7%
Dash Punctuation
ValueCountFrequency (%)
- 28608
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 5291
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 148331
82.4%
Latin 31746
 
17.6%

Most frequent character per script

Common
ValueCountFrequency (%)
2 37866
25.5%
0 34010
22.9%
- 28608
19.3%
1 17008
11.5%
3 8169
 
5.5%
_ 5291
 
3.6%
9 4305
 
2.9%
6 2853
 
1.9%
8 2758
 
1.9%
7 2580
 
1.7%
Other values (2) 4883
 
3.3%
Latin
ValueCountFrequency (%)
n 10582
33.3%
o 10582
33.3%
i 5291
16.7%
f 5291
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180077
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 37866
21.0%
0 34010
18.9%
- 28608
15.9%
1 17008
9.4%
n 10582
 
5.9%
o 10582
 
5.9%
3 8169
 
4.5%
_ 5291
 
2.9%
i 5291
 
2.9%
f 5291
 
2.9%
Other values (6) 17379
9.7%
Distinct1062
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size306.2 KiB
2023-10-24T21:49:02.768861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.1899464
Min length7

Characters and Unicode

Total characters180077
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique477 ?
Unique (%)2.4%

Sample

1st row2023-09-11
2nd row2016-08-21
3rd row2023-09-05
4th row2023-09-11
5th row2023-09-07
ValueCountFrequency (%)
no_info 5291
27.0%
2023-09-10 1285
 
6.6%
2023-09-18 534
 
2.7%
2023-09-17 513
 
2.6%
2023-09-11 485
 
2.5%
2023-02-22 424
 
2.2%
2023-02-21 284
 
1.4%
2023-09-19 249
 
1.3%
2023-06-11 245
 
1.3%
2023-09-03 238
 
1.2%
Other values (1052) 10047
51.3%
2023-10-24T21:49:03.235341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 37201
20.7%
0 33919
18.8%
- 28608
15.9%
3 14395
 
8.0%
1 11045
 
6.1%
n 10582
 
5.9%
o 10582
 
5.9%
9 7134
 
4.0%
_ 5291
 
2.9%
i 5291
 
2.9%
Other values (6) 16029
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 114432
63.5%
Lowercase Letter 31746
 
17.6%
Dash Punctuation 28608
 
15.9%
Connector Punctuation 5291
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 37201
32.5%
0 33919
29.6%
3 14395
 
12.6%
1 11045
 
9.7%
9 7134
 
6.2%
8 3374
 
2.9%
7 2400
 
2.1%
6 1876
 
1.6%
4 1624
 
1.4%
5 1464
 
1.3%
Lowercase Letter
ValueCountFrequency (%)
n 10582
33.3%
o 10582
33.3%
i 5291
16.7%
f 5291
16.7%
Dash Punctuation
ValueCountFrequency (%)
- 28608
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 5291
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 148331
82.4%
Latin 31746
 
17.6%

Most frequent character per script

Common
ValueCountFrequency (%)
2 37201
25.1%
0 33919
22.9%
- 28608
19.3%
3 14395
 
9.7%
1 11045
 
7.4%
9 7134
 
4.8%
_ 5291
 
3.6%
8 3374
 
2.3%
7 2400
 
1.6%
6 1876
 
1.3%
Other values (2) 3088
 
2.1%
Latin
ValueCountFrequency (%)
n 10582
33.3%
o 10582
33.3%
i 5291
16.7%
f 5291
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180077
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 37201
20.7%
0 33919
18.8%
- 28608
15.9%
3 14395
 
8.0%
1 11045
 
6.1%
n 10582
 
5.9%
o 10582
 
5.9%
9 7134
 
4.0%
_ 5291
 
2.9%
i 5291
 
2.9%
Other values (6) 16029
8.9%

review_scores_rating
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size306.2 KiB

review_scores_accuracy
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size306.2 KiB

review_scores_cleanliness
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size306.2 KiB

review_scores_checkin
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size306.2 KiB

review_scores_communication
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size306.2 KiB

review_scores_location
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size306.2 KiB

review_scores_value
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size306.2 KiB
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size172.2 KiB
False
14662 
True
4933 
ValueCountFrequency (%)
False 14662
74.8%
True 4933
 
25.2%
2023-10-24T21:49:03.417984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

calculated_host_listings_count
Real number (ℝ)

HIGH CORRELATION 

Distinct55
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5259505
Minimum1
Maximum163
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size306.2 KiB
2023-10-24T21:49:03.594891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile38
Maximum163
Range162
Interquartile range (IQR)4

Descriptive statistics

Standard deviation21.520368
Coefficient of variation (CV)2.5241019
Kurtosis28.598316
Mean8.5259505
Median Absolute Deviation (MAD)1
Skewness5.0243436
Sum167066
Variance463.12624
MonotonicityNot monotonic
2023-10-24T21:49:03.825308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 9109
46.5%
2 2907
 
14.8%
3 1433
 
7.3%
4 902
 
4.6%
5 551
 
2.8%
6 483
 
2.5%
7 364
 
1.9%
8 332
 
1.7%
10 229
 
1.2%
12 218
 
1.1%
Other values (45) 3067
 
15.7%
ValueCountFrequency (%)
1 9109
46.5%
2 2907
 
14.8%
3 1433
 
7.3%
4 902
 
4.6%
5 551
 
2.8%
6 483
 
2.5%
7 364
 
1.9%
8 332
 
1.7%
9 179
 
0.9%
10 229
 
1.2%
ValueCountFrequency (%)
163 46
 
0.2%
159 159
0.8%
131 9
 
< 0.1%
119 116
0.6%
88 33
 
0.2%
77 77
0.4%
67 67
0.3%
62 59
 
0.3%
58 58
 
0.3%
53 33
 
0.2%

calculated_host_listings_count_entire_homes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct54
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7200306
Minimum0
Maximum159
Zeros2928
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size306.2 KiB
2023-10-24T21:49:04.061431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q33
95-th percentile37
Maximum159
Range159
Interquartile range (IQR)2

Descriptive statistics

Standard deviation21.475058
Coefficient of variation (CV)2.7817322
Kurtosis28.782856
Mean7.7200306
Median Absolute Deviation (MAD)1
Skewness5.0592933
Sum151274
Variance461.17812
MonotonicityNot monotonic
2023-10-24T21:49:04.306364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8772
44.8%
0 2928
 
14.9%
2 2098
 
10.7%
3 936
 
4.8%
4 564
 
2.9%
5 386
 
2.0%
7 284
 
1.4%
6 271
 
1.4%
8 246
 
1.3%
10 189
 
1.0%
Other values (44) 2921
 
14.9%
ValueCountFrequency (%)
0 2928
 
14.9%
1 8772
44.8%
2 2098
 
10.7%
3 936
 
4.8%
4 564
 
2.9%
5 386
 
2.0%
6 271
 
1.4%
7 284
 
1.4%
8 246
 
1.3%
9 124
 
0.6%
ValueCountFrequency (%)
159 159
0.8%
158 46
 
0.2%
131 9
 
< 0.1%
119 116
0.6%
87 33
 
0.2%
77 77
0.4%
64 67
0.3%
62 59
 
0.3%
58 58
 
0.3%
53 33
 
0.2%
Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.69645318
Minimum0
Maximum18
Zeros14342
Zeros (%)73.2%
Negative0
Negative (%)0.0%
Memory size306.2 KiB
2023-10-24T21:49:04.521566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum18
Range18
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.8282165
Coefficient of variation (CV)2.6250386
Kurtosis31.698439
Mean0.69645318
Median Absolute Deviation (MAD)0
Skewness4.9099503
Sum13647
Variance3.3423754
MonotonicityNot monotonic
2023-10-24T21:49:04.739448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 14342
73.2%
1 2476
 
12.6%
2 1077
 
5.5%
3 730
 
3.7%
4 258
 
1.3%
5 249
 
1.3%
6 115
 
0.6%
7 60
 
0.3%
8 55
 
0.3%
12 48
 
0.2%
Other values (7) 185
 
0.9%
ValueCountFrequency (%)
0 14342
73.2%
1 2476
 
12.6%
2 1077
 
5.5%
3 730
 
3.7%
4 258
 
1.3%
5 249
 
1.3%
6 115
 
0.6%
7 60
 
0.3%
8 55
 
0.3%
9 43
 
0.2%
ValueCountFrequency (%)
18 27
0.1%
17 18
 
0.1%
15 38
0.2%
13 18
 
0.1%
12 48
0.2%
11 31
0.2%
10 10
 
0.1%
9 43
0.2%
8 55
0.3%
7 60
0.3%
Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10364889
Minimum0
Maximum15
Zeros19063
Zeros (%)97.3%
Negative0
Negative (%)0.0%
Memory size306.2 KiB
2023-10-24T21:49:04.922319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.90597264
Coefficient of variation (CV)8.7407848
Kurtosis166.85863
Mean0.10364889
Median Absolute Deviation (MAD)0
Skewness12.17906
Sum2031
Variance0.82078642
MonotonicityNot monotonic
2023-10-24T21:49:05.092598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 19063
97.3%
1 220
 
1.1%
2 79
 
0.4%
3 60
 
0.3%
5 34
 
0.2%
4 28
 
0.1%
15 25
 
0.1%
8 25
 
0.1%
13 20
 
0.1%
7 11
 
0.1%
Other values (4) 30
 
0.2%
ValueCountFrequency (%)
0 19063
97.3%
1 220
 
1.1%
2 79
 
0.4%
3 60
 
0.3%
4 28
 
0.1%
5 34
 
0.2%
6 9
 
< 0.1%
7 11
 
0.1%
8 25
 
0.1%
9 7
 
< 0.1%
ValueCountFrequency (%)
15 25
0.1%
13 20
0.1%
12 11
 
0.1%
10 3
 
< 0.1%
9 7
 
< 0.1%
8 25
0.1%
7 11
 
0.1%
6 9
 
< 0.1%
5 34
0.2%
4 28
0.1%

reviews_per_month
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size306.2 KiB

Interactions

2023-10-24T21:48:27.401695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-24T21:46:45.561358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-24T21:46:50.947349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-24T21:46:55.569789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-10-24T21:47:02.489895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-24T21:47:09.082612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-24T21:47:12.939657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-24T21:47:16.751492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-24T21:47:20.581512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-24T21:47:24.499848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-24T21:47:28.342891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-24T21:47:32.463643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-24T21:47:36.166967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-24T21:47:39.924990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-24T21:47:43.971388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-24T21:47:47.776274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-24T21:47:51.753652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-24T21:47:55.535302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-24T21:47:59.374596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-24T21:48:03.248328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-24T21:48:07.271581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-24T21:48:11.072060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-24T21:48:14.887455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-24T21:48:19.041123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-24T21:48:23.040400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-24T21:48:27.238008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-10-24T21:49:05.290769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idhost_idlatitudelongitudeaccommodatespriceminimum_nightsmaximum_nightsminimum_minimum_nightsmaximum_minimum_nightsminimum_maximum_nightsmaximum_maximum_nightsminimum_nights_avg_ntmmaximum_nights_avg_ntmavailability_30availability_60availability_90availability_365number_of_reviewsnumber_of_reviews_ltmnumber_of_reviews_l30dcalculated_host_listings_countcalculated_host_listings_count_entire_homescalculated_host_listings_count_private_roomscalculated_host_listings_count_shared_roomssourcehost_response_timehost_is_superhosthost_verificationshost_has_profile_pichost_identity_verifiedroom_typebathrooms_texthas_availabilityinstant_bookable
id1.0000.415-0.015-0.003-0.057-0.104-0.3030.335-0.285-0.2170.1810.174-0.2870.1740.1130.1100.1070.060-0.383-0.1030.0620.1050.0650.0330.0260.1190.0980.0670.0670.0910.0710.0320.0800.0900.059
host_id0.4151.0000.020-0.092-0.054-0.121-0.2200.133-0.211-0.2390.1120.105-0.2430.1070.1320.1340.1320.062-0.189-0.063-0.002-0.180-0.1960.0120.0700.0840.0930.0970.1220.1940.1200.0630.0550.1020.077
latitude-0.0150.0201.0000.551-0.155-0.326-0.045-0.015-0.046-0.0890.009-0.005-0.0560.0000.001-0.008-0.0040.009-0.035-0.041-0.031-0.083-0.1570.0950.0940.0460.0710.0600.0400.0280.0570.1030.0880.0490.043
longitude-0.003-0.0920.5511.000-0.061-0.1810.078-0.0480.0610.107-0.010-0.0010.097-0.003-0.172-0.204-0.198-0.1550.1640.1640.1270.0460.051-0.0440.0250.0000.1060.0760.0440.0450.0690.0770.1090.0780.059
accommodates-0.057-0.054-0.155-0.0611.0000.5120.110-0.0070.1090.154-0.034-0.0160.130-0.023-0.016-0.023-0.0190.029-0.008-0.0020.0010.0200.292-0.371-0.0760.0150.0370.0370.0170.0030.0240.2950.3070.0460.007
price-0.104-0.121-0.326-0.1810.5121.0000.189-0.0400.2080.165-0.124-0.1210.195-0.1260.1130.1280.1240.122-0.240-0.258-0.225-0.0320.189-0.272-0.1340.0600.1360.0850.0230.0120.0650.1230.2380.1240.047
minimum_nights-0.303-0.220-0.0450.0780.1100.1891.000-0.2270.9400.748-0.147-0.1540.960-0.152-0.138-0.132-0.129-0.148-0.039-0.105-0.111-0.0650.118-0.213-0.1030.0790.0240.0100.0000.0000.0000.0000.0180.0500.018
maximum_nights0.3350.133-0.015-0.048-0.007-0.040-0.2271.000-0.217-0.1590.5640.543-0.2160.5470.1020.1000.1020.177-0.143-0.054-0.0190.2230.1470.0790.0560.0830.0760.0700.0470.0680.0470.0630.0450.0680.042
minimum_minimum_nights-0.285-0.211-0.0460.0610.1090.2080.940-0.2171.0000.705-0.164-0.1750.920-0.172-0.107-0.100-0.099-0.124-0.083-0.149-0.151-0.0710.103-0.198-0.0960.0810.0250.0100.0000.0000.0000.0000.0220.0500.018
maximum_minimum_nights-0.217-0.239-0.0890.1070.1540.1650.748-0.1590.7051.000-0.088-0.0470.854-0.058-0.164-0.157-0.148-0.1400.0840.0640.0560.1160.272-0.209-0.0880.0830.0220.0110.0000.0000.0000.0000.0170.0490.018
minimum_maximum_nights0.1810.1120.009-0.010-0.034-0.124-0.1470.564-0.164-0.0881.0000.941-0.1420.9650.012-0.001-0.0020.0260.0850.1360.1120.1300.0870.0340.0340.0920.1380.1310.0500.0770.0720.0980.0920.0980.337
maximum_maximum_nights0.1740.105-0.005-0.001-0.016-0.121-0.1540.543-0.175-0.0470.9411.000-0.1360.993-0.005-0.018-0.0190.0040.1270.1780.1470.1660.1260.0240.0250.1000.1480.1430.0520.0770.0840.0960.0920.1070.390
minimum_nights_avg_ntm-0.287-0.243-0.0560.0970.1300.1950.960-0.2160.9200.854-0.142-0.1361.000-0.138-0.152-0.145-0.140-0.148-0.004-0.059-0.0680.0010.186-0.222-0.1060.0800.0250.0090.0000.0000.0000.0000.0210.0500.018
maximum_nights_avg_ntm0.1740.1070.000-0.003-0.023-0.126-0.1520.547-0.172-0.0580.9650.993-0.1381.000-0.001-0.014-0.0160.0060.1200.1700.1410.1520.1120.0270.0280.0890.1500.1470.0550.0770.0840.0930.0880.1060.371
availability_300.1130.1320.001-0.172-0.0160.113-0.1380.102-0.107-0.1640.012-0.005-0.152-0.0011.0000.9610.9320.619-0.270-0.226-0.157-0.004-0.1210.1540.0940.5350.1960.1980.0410.0540.0960.1290.0690.2030.121
availability_600.1100.134-0.008-0.204-0.0230.128-0.1320.100-0.100-0.157-0.001-0.018-0.145-0.0140.9611.0000.9820.654-0.297-0.254-0.177-0.011-0.1290.1580.0930.5900.2250.2150.0440.0550.1160.1350.0710.2240.124
availability_900.1070.132-0.004-0.198-0.0190.124-0.1290.102-0.099-0.148-0.002-0.019-0.140-0.0160.9320.9821.0000.677-0.288-0.243-0.167-0.007-0.1230.1530.0950.6430.2390.2170.0420.0540.1260.1310.0680.2420.114
availability_3650.0600.0620.009-0.1550.0290.122-0.1480.177-0.124-0.1400.0260.004-0.1480.0060.6190.6540.6771.000-0.202-0.161-0.1270.002-0.0870.1090.0690.7020.2010.1680.0420.0470.1040.0960.0650.2590.073
number_of_reviews-0.383-0.189-0.0350.164-0.008-0.240-0.039-0.143-0.0830.0840.0850.127-0.0040.120-0.270-0.297-0.288-0.2021.0000.8720.5580.0670.142-0.132-0.0740.0510.0900.1780.0200.0270.0720.0450.0000.0420.076
number_of_reviews_ltm-0.103-0.063-0.0410.164-0.002-0.258-0.105-0.054-0.1490.0640.1360.178-0.0590.170-0.226-0.254-0.243-0.1610.8721.0000.6760.0910.176-0.153-0.0700.1130.1650.2860.0210.0420.0830.0720.0350.0730.124
number_of_reviews_l30d0.062-0.002-0.0310.1270.001-0.225-0.111-0.019-0.1510.0560.1120.147-0.0680.141-0.157-0.177-0.167-0.1270.5580.6761.0000.0570.135-0.134-0.0560.1220.1590.1990.0000.0200.0650.0560.0290.0670.109
calculated_host_listings_count0.105-0.180-0.0830.0460.020-0.032-0.0650.223-0.0710.1160.1300.1660.0010.152-0.004-0.011-0.0070.0020.0670.0910.0571.0000.7120.2500.1340.0630.1470.1110.0860.0370.1040.0730.1250.0530.221
calculated_host_listings_count_entire_homes0.065-0.196-0.1570.0510.2920.1890.1180.1470.1030.2720.0870.1260.1860.112-0.121-0.129-0.123-0.0870.1420.1760.1350.7121.000-0.363-0.1570.0570.1460.1180.0900.0260.1020.0940.1250.0520.221
calculated_host_listings_count_private_rooms0.0330.0120.095-0.044-0.371-0.272-0.2130.079-0.198-0.2090.0340.024-0.2220.0270.1540.1580.1530.109-0.132-0.153-0.1340.250-0.3631.0000.1860.0290.0690.0700.0500.0230.0300.3290.2280.0370.125
calculated_host_listings_count_shared_rooms0.0260.0700.0940.025-0.076-0.134-0.1030.056-0.096-0.0880.0340.025-0.1060.0280.0940.0930.0950.069-0.074-0.070-0.0560.134-0.1570.1861.0000.0080.0480.0290.0090.0000.0080.3440.2320.0000.086
source0.1190.0840.0460.0000.0150.0600.0790.0830.0810.0830.0920.1000.0800.0890.5350.5900.6430.7020.0510.1130.1220.0630.0570.0290.0081.0000.3210.0860.0780.0310.1100.0100.0000.3610.062
host_response_time0.0980.0930.0710.1060.0370.1360.0240.0760.0250.0220.1380.1480.0250.1500.1960.2250.2390.2010.0900.1650.1590.1470.1460.0690.0480.3211.0000.2980.0630.0730.1910.0740.0820.2630.230
host_is_superhost0.0670.0970.0600.0760.0370.0850.0100.0700.0100.0110.1310.1430.0090.1470.1980.2150.2170.1680.1780.2860.1990.1110.1180.0700.0290.0860.2981.0000.0900.0640.1020.0580.0610.1000.068
host_verifications0.0670.1220.0400.0440.0170.0230.0000.0470.0000.0000.0500.0520.0000.0550.0410.0440.0420.0420.0200.0210.0000.0860.0900.0500.0090.0780.0630.0901.0000.7170.7110.0400.0160.1450.054
host_has_profile_pic0.0910.1940.0280.0450.0030.0120.0000.0680.0000.0000.0770.0770.0000.0770.0540.0550.0540.0470.0270.0420.0200.0370.0260.0230.0000.0310.0730.0640.7171.0000.7080.0000.0000.0700.022
host_identity_verified0.0710.1200.0570.0690.0240.0650.0000.0470.0000.0000.0720.0840.0000.0840.0960.1160.1260.1040.0720.0830.0650.1040.1020.0300.0080.1100.1910.1020.7110.7081.0000.0430.0370.2280.059
room_type0.0320.0630.1030.0770.2950.1230.0000.0630.0000.0000.0980.0960.0000.0930.1290.1350.1310.0960.0450.0720.0560.0730.0940.3290.3440.0100.0740.0580.0400.0000.0431.0000.5400.0220.043
bathrooms_text0.0800.0550.0880.1090.3070.2380.0180.0450.0220.0170.0920.0920.0210.0880.0690.0710.0680.0650.0000.0350.0290.1250.1250.2280.2320.0000.0820.0610.0160.0000.0370.5401.0000.0710.093
has_availability0.0900.1020.0490.0780.0460.1240.0500.0680.0500.0490.0980.1070.0500.1060.2030.2240.2420.2590.0420.0730.0670.0530.0520.0370.0000.3610.2630.1000.1450.0700.2280.0220.0711.0000.093
instant_bookable0.0590.0770.0430.0590.0070.0470.0180.0420.0180.0180.3370.3900.0180.3710.1210.1240.1140.0730.0760.1240.1090.2210.2210.1250.0860.0620.2300.0680.0540.0220.0590.0430.0930.0931.000

Missing values

2023-10-24T21:48:31.901958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-24T21:48:32.727341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idlisting_urllast_scrapedsourcenamedescriptionneighborhood_overviewpicture_urlhost_idhost_urlhost_namehost_sincehost_locationhost_abouthost_response_timehost_response_ratehost_acceptance_ratehost_is_superhosthost_thumbnail_urlhost_picture_urlhost_neighbourhoodhost_listings_counthost_total_listings_counthost_verificationshost_has_profile_pichost_identity_verifiedneighbourhoodneighbourhood_cleansedlatitudelongitudeproperty_typeroom_typeaccommodatesbathrooms_textbedroomsbedsamenitiespriceminimum_nightsmaximum_nightsminimum_minimum_nightsmaximum_minimum_nightsminimum_maximum_nightsmaximum_maximum_nightsminimum_nights_avg_ntmmaximum_nights_avg_ntmhas_availabilityavailability_30availability_60availability_90availability_365calendar_last_scrapednumber_of_reviewsnumber_of_reviews_ltmnumber_of_reviews_l30dfirst_reviewlast_reviewreview_scores_ratingreview_scores_accuracyreview_scores_cleanlinessreview_scores_checkinreview_scores_communicationreview_scores_locationreview_scores_valueinstant_bookablecalculated_host_listings_countcalculated_host_listings_count_entire_homescalculated_host_listings_count_private_roomscalculated_host_listings_count_shared_roomsreviews_per_month
0231497https://www.airbnb.com/rooms/2314972023-09-22city scrapeRental unit in Rio de Janeiro · ★4.73 · 1 bedroom · 1 bed · 1 baththis is a big studio at the end of copacabana walking distance to arpoador and ipanema which can accomodate persons in a double bed and another on couches which can be opened to sleep onclose to commercial area and public transportationthe spacespacious flat with double bed air conditioner tv linen ceiling fan small kitchen and bathroom there is also sofabed which can accommodate more people for an additional charge of us each hour doorman walking distance to ipanema the flat is very well located near the commercial area supermarkets restaurants banks bars and night clubs the very end of copacabana is a much better area to stay due to its proximity to ipanema you also have means of transport to anywhere in rio buses subway theres is a subway station two blocks away from the flat and taxis you are very close to the fortress of copacabana a main touristic attraction where you hano_infohttps://a0.muscache.com/pictures/3582382/ee8acc55_original.jpg1207700https://www.airbnb.com/users/show/1207700Maria Luiza2011-09-25Rio de Janeiro, BrazilMeu nome é Maria Luiza, adoro ajudar meus hóspedes, pois vivi muito tempo no exterior, falo várias línguas, e entendo como é viver fora da sua cidade.\n\n \nFalo e escrevo em Inglês, francês, espanhol e português.within a few hours100%82%fhttps://a0.muscache.com/im/users/1207700/profile_pic/1316987019/original.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/users/1207700/profile_pic/1316987019/original.jpg?aki_policy=profile_x_mediumCopacabana4.08.0['email', 'phone']ttno_infoCopacabana-22.98238-43.19215Entire rental unitEntire home/apt41 bath1.01.0["Hangers", "Body soap", "Elevator", "Bed linens", "Microwave", "Wifi", "Dishes and silverware", "Hair dryer", "TV", "Iron", "Dining table", "Ceiling fan", "Essentials", "Public or shared beach access \u2013 Beachfront", "Refrigerator", "Coffee maker: drip coffee maker", "Gas stove", "Hot water", "Extra pillows and blankets", "Kitchen", "Air conditioning"]180.03893389893.089.0t0002042023-09-2278912011-11-172023-09-114.734.834.864.894.924.94.65f44000.54
1231516https://www.airbnb.com/rooms/2315162023-09-22city scrapeRental unit in Rio de Janeiro · ★4.71 · 1 bedroom · 1 bedspecial location of the building on copacabana beach although the apartment does not have an ocean view but its great for people who love the sea and for the ones staying for new years accomodates up to personsthe spacespacious apartment with one bedroom comfortable double bed air conditioner tv linenlivingroom with a double sofabed more people can be accommodated small kitchen bathroom ceiling fans hour doorman walking distance to ipanema the building is on copacabana beach but the apartment does not have bech view its at the back of the building facing the street behindthe apartment is very well located near the commercial area supermarkets restaurants banks bars and night clubs the very end of copacabana is a much better area to stay due to its proximity to ipanema you are very close to the fortress of copacabana a main tourist attraction where you have the most fantastic view of the famous copacabno_infohttps://a0.muscache.com/pictures/3671683/d74b44a4_original.jpg1207700https://www.airbnb.com/users/show/1207700Maria Luiza2011-09-25Rio de Janeiro, BrazilMeu nome é Maria Luiza, adoro ajudar meus hóspedes, pois vivi muito tempo no exterior, falo várias línguas, e entendo como é viver fora da sua cidade.\n\n \nFalo e escrevo em Inglês, francês, espanhol e português.within a few hours100%82%fhttps://a0.muscache.com/im/users/1207700/profile_pic/1316987019/original.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/users/1207700/profile_pic/1316987019/original.jpg?aki_policy=profile_x_mediumCopacabana4.08.0['email', 'phone']ttno_infoCopacabana-22.97787-43.18792Entire rental unitEntire home/apt4no_info1.01.0["TV", "Kitchen", "Wifi", "Elevator", "Air conditioning"]350.03893389893.089.0t0011132023-09-2229002011-11-022016-08-214.714.764.524.794.864.934.38f44000.2
2236991https://www.airbnb.com/rooms/2369912023-09-23city scrapeRental unit in Rio de Janeiro · ★4.89 · 1 bedroom · 4 beds · 1 bathaconchegante amplo bsico arejado iluminado com luz natural em prdio seguro e familiar prdio com portaria horas e cameras de segurana em todos os andares do edifcio tudo isto em copacabana a quase quadra do mar o segundo prdio da segunda quadra da praia est localizado na av prado junior quase esquina com av nsra de copacabanathe spaceo apartamento possui moblia bsica mas a necessria para voce se sentir em um espao limpo confortvel e aconchegante tambm tem os eletrodomesticos bsicos que no podem faltar em um apto como microondas cafeteira eltrica mquina de lavar fogo tv e geladeira todos a volts e um guardaroupas grande onde voc pode colocar suas malas roupas e pertences na sala h uma mesa com cadeiras um sof cama casal tipo fouton no quarto uma cama box de casal ortobom master pocket de molas ensacadas e camas de solteiro uma delas ortobom e bicama o apto tambm tem ar condicionado e ventilCopacabana, apelidada a princesinha do mar, faz juz ao apelido.<br />Além de possuir uma das praias mais famosas e charmosas do Rio de Janeiro fornece ao turista ampla estrutura com variedade de restaurantes, agências de turismos, casas de câmbio, supermercados, drogarias, e a poucos passos um grande shopping (Rio Sul) e etc.https://a0.muscache.com/pictures/5725a59b-147d-4bf2-99f2-ba67f55ee770.jpg1241662https://www.airbnb.com/users/show/1241662Nilda2011-10-03Rio de Janeiro, BrazilHellow ! Im Nilda! I love Rio de Janeiro. I work renting apartments for short time. the places are simples! but very clean , safe and well provided with basic staffs to spent a great vacations.\n\nVery well located, next to the beach one of the most famous Rio de Janeiro´ s beach: Copacabana you ll have easy and plenty access by bus and others public \n transportations services to the main and classic touristic points more visited by the travellers in Rio de Janeiro.\n\nWelcome to Rio, welcome to Brazil! \n\nwithin an hour100%96%thttps://a0.muscache.com/im/pictures/user/fea78163-5495-401a-a620-ed948f59ac91.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/fea78163-5495-401a-a620-ed948f59ac91.jpg?aki_policy=profile_x_mediumCopacabana2.02.0['email', 'phone']ttRio de Janeiro, BrazilCopacabana-22.96488-43.17478Entire rental unitEntire home/apt51 bath1.04.0["Clothing storage: wardrobe", "Public or shared beach access", "Hangers", "Esmaltec gas stove", "Elevator", "Cooking basics", "32\" HDTV", "Room-darkening shades", "Bed linens", "Microwave", "Free washer \u2013 In unit", "Drying rack for clothing", "Dishes and silverware", "Cleaning products", "Courtyard view", "Coffee maker", "Iron", "Laundromat nearby", "Dining table", "Ceiling fan", "Blender", "Essentials", "Hot water kettle", "Refrigerator", "Host greets you", "EV charger", "Mountain view", "Hot water", "Oven", "Kitchen", "Window AC unit", "Wifi \u2013 14 Mbps"]190.051447112511255.01125.0t142151512023-09-23762422014-03-032023-09-054.894.964.914.974.964.994.89f22000.65
317878https://www.airbnb.com/rooms/178782023-09-23city scrapeCondo in Rio de Janeiro · ★4.70 · 2 bedrooms · 2 beds · 1 bathplease note that elevated rates applies for new years and carnival price depends on length of stay and number of people generally i prefer a stay for week or more and a maximum of people at the most contact me and we will discuss bright and sunny large balcony square meters high speed wifi up to mb smart tv you can watch netflix etc if you have an account h doorman minute to walk to copacabana beach silent split air conditioning best spot in riothe space beautiful sunny bedroom square meters in h doorman building min to walk to copacabana beach spacious living room bedrooms with fullsize beds each sleeps large balcony which looks out on pedestrian street no traffic priceless in rio apts with sea view are noisy because of traffic split air condition in each room almost silent like in a hotel smart tThis is the one of the bests spots in Rio. Because of the large balcony and proximity to the beach, it has huge advantages in the current situation.https://a0.muscache.com/pictures/65320518/30698f38_original.jpg68997https://www.airbnb.com/users/show/68997Matthias2010-01-08Rio de Janeiro, BrazilI am a journalist/writer. Lived in NYC for 15 years. I am now based in Rio and published 3 volumes of travel stories on AMAZ0N: "The World Is My Oyster". If you have never been to Rio, check out the first story, and you'll get an idea. Apart from Rio, you'll find 29 other travel stories from all around the globe.within an hour100%96%thttps://a0.muscache.com/im/pictures/user/67b13cea-8c11-49c0-a08d-7f42c330676e.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/67b13cea-8c11-49c0-a08d-7f42c330676e.jpg?aki_policy=profile_x_mediumCopacabana2.05.0['email', 'phone']ttRio de Janeiro, BrazilCopacabana-22.96599-43.17940Entire condoEntire home/apt51 bath2.02.0["Patio or balcony", "Hangers", "Paid parking off premises", "Elevator", "Cooking basics", "Private entrance", "Bed linens", "Microwave", "Wifi", "Dishes and silverware", "Hair dryer", "Self check-in", "Iron", "Building staff", "Essentials", "TV with standard cable", "Refrigerator", "Stove", "Smoking allowed", "Bathtub", "Hot water", "Oven", "Air conditioning", "Kitchen", "Luggage dropoff allowed", "Coffee maker"]279.05285528285.028.0t1421382652023-09-233012512010-07-152023-09-114.74.774.644.834.914.774.67f11001.87
525026https://www.airbnb.com/rooms/250262023-09-22city scrapeRental unit in Rio de Janeiro · ★4.71 · 1 bedroom · 1 bed · 1 bathfully renovated in dec new kitchen new bathroom new flooring o apto foi todo renovado piso banheiro e cozinha novos em dez se vc nao tem opiniario no airbnb e nunca usou antes por favor mande mensagem antes falando quem vc our apartment is a little gem everyone loves staying there best location blocks to the subway blocks to the beach close to bars restaurants supermarkets subway wifi cable tv air con and fanthe spacethis newly renovated studio fully renovated dec is in the best location of copacabana situated on a quieter street but just off the main streets right in the middle of everything blocks from the beach block from the subway cantagalo station which places you just a stop away from ipanema you can just walk there too no need to hop on the subway really very close to all local bars and restaurants and very close to ipanema and lagoa walking disCopacabana is a lively neighborhood and the apartment is located very close to an area in Copa full of bars, cafes and restaurants at Rua Bolivar and Domingos Ferreira. Copacabana never sleeps, there is always movement and it's a great mix of all kinds of people.https://a0.muscache.com/pictures/a745aa21-b8dd-4959-a040-eb8e6e6f07ee.jpg102840https://www.airbnb.com/users/show/102840Viviane2010-04-03Rio de Janeiro, BrazilHi guys,\n\nViviane is a commercial photographer, an avid world traveler, (a former photographer for Airbnb) and an Airbnb superhost. And a free lance photographer for other wonderful clients. She loves life and meeting people.\n\nWe work together in providing the best accommodation to people and we are\nfirm believers of enjoying the moment as a prime attitude towards life!\nwithin a few hours100%73%thttps://a0.muscache.com/im/pictures/user/315ddc81-bea3-4bf0-8fc7-be197a6541ff.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/315ddc81-bea3-4bf0-8fc7-be197a6541ff.jpg?aki_policy=profile_x_mediumCopacabana1.05.0['email', 'phone']ttRio de Janeiro, BrazilCopacabana-22.97735-43.19105Entire rental unitEntire home/apt31 bath1.01.0["Clothing storage: wardrobe", "Dedicated workspace", "Public or shared beach access", "Hangers", "Elevator", "Cooking basics", "Room-darkening shades", "Bed linens", "Single level home", "Microwave", "Wifi", "Drying rack for clothing", "Dishes and silverware", "Hair dryer", "Iron", "Laundromat nearby", "Paid street parking off premises", "Dining table", "Ceiling fan", "Books and reading material", "Blender", "Essentials", "Hammock", "TV with standard cable", "Refrigerator", "Stove", "Host greets you", "Hot water", "Oven", "Kitchen", "Window AC unit", "Coffee maker"]330.02602260602.060.0t611412032023-09-222722412010-06-072023-09-074.714.694.784.814.924.844.59f11001.68
6238802https://www.airbnb.com/rooms/2388022023-09-23city scrapeRental unit in Rio · ★4.77 · 3 bedrooms · 4 beds · 2 bathsspacious clean and bright bedroom apartment with bathrooms located at only meters from the famous copacabana beachthe spacefully furnished three bedroom apartment located in copacabana this spacious apartment in copacabana is the right pick for a group of friends or a family this apartment have three bedrooms with double beds a double sofa bed in the living room that can accommodate up to people two complete bathrooms all bedrooms and living room come with split air conditioningwifi is available for your use the kitchen is equipped with stove fridgefreezer microwave coffee maker other electrical appliances and washing machinebed linens and bath towels are includedplease note for long term reservations the utility bills electricity and gas are not included in the rent and will be pay separatelyguest accesswe will meet our gueWhat makes Copacabana authentic is mainly because of its beautiful beach and striking pavement pattern. Day in and day out thousands of Cariocas and tourists stroll and jog on the famous wavy mosaic pavement in black and white marble, designed by Roberto Burle Marx.https://a0.muscache.com/pictures/46b95cc9-d4d6-4a90-b444-5ddaf9ecca11.jpg235496https://www.airbnb.com/users/show/235496Paola E Isak2010-09-15Rio de Janeiro, BrazilSwedish-Colombian couple in love with Rio & Barcelona, always looking for stunning sunsets! \n\nWe will be available by phone during your stay to ensure a smooth and unforgettable experience and to attend any questions you might have. \n_\n\nCasal sueco-colombiano apaixonado pelo Rio e Barcelona.\n\nEstaremos sempre disponíveis por telefone durante sua estadia para atender a quaisquer dúvidas que possam ter e para garantir uma experiência inesquecível.within an hour100%100%thttps://a0.muscache.com/im/pictures/user/75f13205-20fa-4b70-bd73-71f4b995b58a.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/75f13205-20fa-4b70-bd73-71f4b995b58a.jpg?aki_policy=profile_x_mediumCopacabana5.010.0['email', 'phone']ttRio, Rio de Janeiro, BrazilCopacabana-22.96386-43.17757Entire rental unitEntire home/apt82 baths3.04.0["Freezer", "Hangers", "Body soap", "Beach access", "Carbon monoxide alarm", "Kitchen", "Elevator", "Cooking basics", "Room-darkening shades", "Heating", "Baking sheet", "Wine glasses", "Bed linens", "40\" TV with standard cable", "Window guards", "Microwave", "Shower gel", "Free washer \u2013 In unit", "AC - split type ductless system", "Drying rack for clothing", "Smoke alarm", "Wifi", "Cleaning products", "Dishes and silverware", "Ethernet connection", "Hair dryer", "Private patio or balcony", "Coffee maker", "Iron", "Laundromat nearby", "Long term stays allowed", "Dining table", "Toaster", "Waterfront", "Essentials", "First aid kit", "Clothing storage", "Refrigerator", "Host greets you", "Bathtub", "Gas stove", "Hot water", "Oven", "Portable fans", "Extra pillows and blankets"]642.0218027112511252.31125.0t2534512892023-09-23973322011-11-172023-09-174.774.864.874.94.934.814.64t44000.67
7239531https://www.airbnb.com/rooms/2395312023-09-23city scrapeRental unit in Rio de Janeiro · ★4.58 · 1 bedroom · 2 beds · 1 bathvaranda with a seaside view from the eleventh floor bedroom with a double bed air internetwifi good bathroom living with a sofabed ceiling fan and cable tv and complete kitchen all surrounded with all you may need and steps to the beachvaranda com vista lateral do mar do dcimo primeiro andar quarto com cama de casal ar internetwifi bom banheiro sala com sofcama tv a cabo e ventilador de teto e cozinha completa tudo rodeado de todas as facilidades a passos da praiathe spacebeautiful and quiet one bedroom apartment just meters from copacabana beach on the eleventh floor a very comfortable bedroom with a double bed good linen with soft and fresh shower and face towels in the living room there is a sofa bed accommodate two people a ceiling fan a lcd cable tv high speed internet with wireless connection mb a comfortable bathroom with automatic gas heater and hygienic showerthe kitchen is all eno_infohttps://a0.muscache.com/pictures/12451384/18c18103_original.jpg792218https://www.airbnb.com/users/show/792218Levy2011-07-07Rio de Janeiro, BrazilI live with my wife in Copacabana, Rio, where I was born. I'm a Rio's lover and like to host people from all over the world in our apartments in Copacabana/ Ipanema, with all the necessary tips for a nice stay.\r\nEu vivo com minha esposa em Copacabana, Rio, aonde nasci. Apaixonado pelo Rio, gosto de receber pessoas de todas as partes do mundo nos nossos apartamentos em Copacabana e Ipanema, dando todas as necessárias dicas para uma boa estadia.within an hour100%97%fhttps://a0.muscache.com/im/users/792218/profile_pic/1310233853/original.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/users/792218/profile_pic/1310233853/original.jpg?aki_policy=profile_x_mediumCopacabana10.015.0['email', 'phone']ttno_infoCopacabana-22.96434-43.17563Entire rental unitEntire home/apt41 bath1.02.0["Patio or balcony", "Hangers", "Paid parking off premises", "Elevator", "Cooking basics", "Room-darkening shades", "Bed linens", "Pocket wifi", "Microwave", "Paid parking on premises", "Wifi", "Ocean view", "Dishes and silverware", "Ethernet connection", "Hair dryer", "Coffee maker", "Iron", "Fire extinguisher", "Long term stays allowed", "Beach access \u2013 Beachfront", "Beach view", "Babysitter recommendations", "Essentials", "High chair", "TV with standard cable", "Refrigerator", "Stove", "Host greets you", "Hot water", "Extra pillows and blankets", "Oven", "Kitchen", "Air conditioning"]200.02150221501502.0150.0t513433182023-09-231091222011-12-082023-09-214.584.664.334.794.814.74.42f1010000.76
835764https://www.airbnb.com/rooms/357642023-09-23city scrapeLoft in Rio de Janeiro · ★4.90 · 1 bedroom · 1 bed · 1.5 bathsour newly renovated studio is located in the best part of copacabana between posto and posto minutes from the arpoador and ipanema beach security hours a day enjoy your stay in a family bulding living as a local people please check the possibility of flexible checkin and checkout timesthe spacefeel like your home living as carioca local people in a new renovated and refurbished apartment carefully prepared for you the apartment is very clean in a safe and familiar building only studios per floor make sure you will stay in the best place of copacabana posto lifeguard station minutes walking to arpoador and ipanema attention all the pictures are surrounding the apartment and not far away we are pround super host with also full golden stars in all aspects we care about your well beingpackages for carnival is available send a message to uscheck in andOur guests will experience living with a local peole "Carioca" in a very friendly building with 24 hours a day security with all kind of stores, banks, transports, restaurants.https://a0.muscache.com/pictures/23782972/1d3e55b0_original.jpg153691https://www.airbnb.com/users/show/153691Patricia Miranda & Paulo2010-06-27Rio de Janeiro, BrazilHello, We are Patricia Miranda and Paulo.\r\nWe are a couple who love to meet new people, new cultures, we both are very easy going persons, we love to travel, music, dancing, to walk along the beach and chat with friends in a bar in somewhere in Rio. We are retired after working for several years in tourism and an international airline company. Our biggest passion is playing with our 4 american granddaughters . We are gay friendly and everybody is welcome! We look forward to meeting you and sharing advice and recommendation about our wonderful city.\r\n\r\nWe were guest in Buenos Aires, João Pessoa, PB, Brooklin NY, and Manhattan.\r\n\r\n\r\n!within an hour100%96%thttps://a0.muscache.com/im/users/153691/profile_pic/1277774787/original.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/users/153691/profile_pic/1277774787/original.jpg?aki_policy=profile_x_mediumCopacabana1.02.0['email', 'phone']ttRio de Janeiro, BrazilCopacabana-22.98107-43.19136Entire loftEntire home/apt21.5 baths1.01.0["32\" HDTV with standard cable", "Hangers", "Carbon monoxide alarm", "Paid parking off premises", "Elevator", "Cooking basics", "Room-darkening shades", "Heating", "Bed linens", "Pocket wifi", "Microwave", "Wifi", "Smoke alarm", "Dishes and silverware", "Hair dryer", "Coffee maker", "Self check-in", "Iron", "Fire extinguisher", "Building staff", "Beach access \u2013 Beachfront", "Beach view", "Essentials", "Refrigerator", "Stove", "Hot water", "Kitchen", "Window AC unit", "Luggage dropoff allowed", "Extra pillows and blankets"]192.0315357153.014.8t4811462023-09-234463712010-10-032023-09-114.94.934.934.974.954.944.89f11002.82
10245951https://www.airbnb.com/rooms/2459512023-09-23city scrapeRental unit in Rio de Janeiro · ★4.86 · 1 bedroom · 2 beds · 1 bathgreat space for those who need leisure as well as work we have megs of wifi internet and btus split air conditioning for the greater comfort of your homeofficethe spacecomplete and luxurious apartment blocks from the beach in copacabana the most famous beach in rio de janeiro where one can walk swim in the sea get a tan cycling and exercising outdoors a short walk from the ipanema beach the second most famous beach in rio de janeiro and place that inspired the composer vinicius de moraes when he launched the popular song girl from ipanema litter box for couples sofa beds cable tv internet bed linen sheets lines telephone intercom with concierge full kitchen cabinets with drawers and a safe located close to subway bus stop and taxi making easy access to major sights of rio de janeiro in the region has the best bars and restaurants it is also possible to find all types of trade as pharmaciesno_infohttps://a0.muscache.com/pictures/41861094/4e098c3e_original.jpg1289982https://www.airbnb.com/users/show/1289982Carnaval Turismo2011-10-14Rio de Janeiro, BrazilCarnaval Turismo é uma agência de viagens que tem como produto principal o Carnaval e o receptivo no Rio de Janeiro. A Cidade Maravilhosa e um produto como o Carnaval que espelham a alegria de viver e a simpatia do povo carioca nos estimulam a receber BEM os nossos hóspedes. Copacabana é uma " Cidade" : Praia, Sol, Samba, Carnaval, Animação e Conforto na hospedagem, é o melhor que um turista pode desejar ao chegar em uma cidade tão receptiva como o Rio de Janeiro! Welcome!! \nImportante: \nMANTEMOS UM RIGOROSO PROTOCOLO SANITÁRIO DE HOSPEDAGEM DURANTE A PANDEMIA DE COVID-19 ! TOTAL PROTEÇÃOPARA OS NOSSOS HOSPEDES ! \nwithin a few hours100%83%fhttps://a0.muscache.com/im/pictures/user/23c803d9-09b5-4692-b4fb-bed9c73c769a.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/23c803d9-09b5-4692-b4fb-bed9c73c769a.jpg?aki_policy=profile_x_mediumCopacabana1.01.0['email', 'phone']ttno_infoCopacabana-22.96704-43.18201Entire rental unitEntire home/apt41 bath1.02.0["Private entrance", "Shampoo", "Essentials", "Hair dryer", "Bed linens", "TV with standard cable", "Refrigerator", "Dishes and silverware", "Stove", "Coffee maker", "Hangers", "Microwave", "Hot water", "Iron", "Kitchen", "Window AC unit", "Wifi", "Elevator", "Extra pillows and blankets", "Cooking basics"]300.03760247607603.0760.0t3056863422023-09-2314102012-03-052023-06-114.865.05.04.864.934.864.86f11000.1
1148305https://www.airbnb.com/rooms/483052023-09-22city scrapeRental unit in Ipanema · ★4.74 · 6 bedrooms · 7 beds · 7 bathsenter bossa novas history by staying in the very street reallife girl from ipanema used to each day walk to the sea penthouse with side ocean view six ensuite bedrooms prime location first beachblock heart of ipanema exceptional comfort beachy modern midcentury style designed for digital nomads remotework ready lightningfast internet ms locallyrooted home experiencedriven stay dedicated team authentic and unforgettable rio storythe spacelocated on the first beach block this beachy modern midcentury styled penthouse was designed in a thoughtful way for modern travellersmore than having a place to sleep we understand that guests look for remotework ready locally rooted and experiencedriven properties with a dedicated team taking pride in going the extra mileEnter Bossa Nova history by staying in the very street where real-life 'Girl From Ipanema' inspired Vinicius de Moraes to write the worldwide famous song, "each day when she walks to the sea".<br /><br />Located seconds from Ipanema Beach's best spot Posto 9, on the first beach block in the heart of Ipanema’s very best neighbourhood, enjoy staying in this elegant and modern hideaway in a peaceful residential street.<br /><br />Ipanema and Leblon are by far the safest locations in Rio, with popular restaurants, cafes and boutique stores walking distance to the apartment.https://a0.muscache.com/pictures/miso/Hosting-48305/original/cce14bf9-c5b6-44c6-a89f-61323975afdb.jpeg70933https://www.airbnb.com/users/show/70933Goitaca2010-01-16Rio de Janeiro, BrazilA new frontier of hospitality\n\nThe word meaning ‘Nomad’ in the native language of Tupi Guarani, Goitaca symbolizes a new genre of travel, one built with the modern traveler in mind. With locally-rooted and experience-driven stays in homes that are intentionally designed, fully-equipped, and remote-work ready, we’re driven to shift the role of hospitality in the world. Bringing the comfort of a hotel together with the privacy of a home, our residences and customized care provide unique experiences for our guests. \n\nExperience-driven stays that nurture our guests’ well-being \n\nThe well-being and satisfaction of our guests are our utmost focus and we are determined to continue improving the quality and depth of the experiences and stories we’re creating. We believe in delivering remarkable hospitality that is also accessible. By designing, developing, and operating our residences ourselves, we provide unique, personalized stays at a fraction of the cost and pass those savings back to our guests.\n\nA tribute to Brazil\n\nOur wish is that our guests may discover and experience the magic, beauty, and ***alegria*** of Rio, while paying homage to its history and multi-faceted culture.\n\nBehind Goitaca stands a small and dedicated local team who take pride in going the extra mile to ensure our guests live an easy, authentic, and unforgettable Rio story. Working together since 2011, we have been lucky to welcome over 2,000 guests and continue to connect and learn from them.within an hour100%97%thttps://a0.muscache.com/im/pictures/user/c2d77835-fab6-45f9-a3ef-ef570dda44a5.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/c2d77835-fab6-45f9-a3ef-ef570dda44a5.jpg?aki_policy=profile_x_mediumIpanema11.032.0['email', 'phone', 'work_email']ttIpanema, Rio de Janeiro, BrazilIpanema-22.98591-43.20302Entire rental unitEntire home/apt137 baths6.07.0["BBQ grill", "Hangers", "Paid dryer \u2013 In unit", "Carbon monoxide alarm", "Paid parking off premises", "Cooking basics", "Private entrance", "Shampoo", "Room-darkening shades", "Bed linens", "Sea view", "40\" HDTV with Chromecast, premium cable", "Microwave", "Pack \u2019n play/Travel crib", "Wifi", "AC - split type ductless system", "Smoke alarm", "Dishes and silverware", "Hair dryer", "Private patio or balcony", "Coffee maker", "Crib", "Iron", "Fire extinguisher", "Long term stays allowed", "Beach view", "Essentials", "Baby bath", "Refrigerator", "Stove", "Host greets you", "Hot water", "Oven", "Kitchen", "Luggage dropoff allowed", "Extra pillows and blankets"]3448.02892589892.489.0t1327483062023-09-221523042011-03-022023-09-104.744.694.694.824.814.944.59t98100.99
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31952984813172702446479https://www.airbnb.com/rooms/9848131727024464792023-09-22city scrapeRental unit in Rio de Janeiro · ★New · 1 bedroom · 2 beds · 2 bathso grupo ter fcil acesso a tudo o que precisar neste lugar com excelente localizao utenslios domsticos o flat com com fogo a gs para no ficar refem a comida da rua utenslios bsicos the spaceflat funcional tima localizao condomnio seguro voltado para rea empresarial recepo horas conhea nosso espaco ser um prazer em lhe ter como hspede other things to noteflat com sistema de hotel restaurante dentro do condomnio onde poder usar full timeno_infohttps://a0.muscache.com/pictures/prohost-api/Hosting-984813172702446479/original/0c637234-7cca-497c-b3fe-9af83bc8e450.jpeg503285061https://www.airbnb.com/users/show/503285061Pedro2023-02-28Rio de Janeiro, BrazilSou uma pessoa , ligada aí desafios , gosto do novo , seja bem vindowithin an hour100%96%thttps://a0.muscache.com/im/pictures/user/User-503285061/original/3974b853-e8c6-4012-afac-7df1dbbe0ddf.jpeg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/User-503285061/original/3974b853-e8c6-4012-afac-7df1dbbe0ddf.jpeg?aki_policy=profile_x_mediumJacarepaguá17.020.0['email', 'phone']ttno_infoCamorim-22.981866-43.423547Entire rental unitEntire home/apt62 baths1.02.0["Free parking on premises", "First aid kit", "Kitchen", "Hair dryer", "Dedicated workspace", "Washer", "TV", "Carbon monoxide alarm", "Air conditioning", "Fire extinguisher", "Pets allowed", "Wifi", "Smoke alarm", "Cooking basics"]285.01891389891.189.0t3060903642023-09-22000no_infono_infono_infono_infono_infono_infono_infono_infono_infot171700no_info
31954984840932646693948https://www.airbnb.com/rooms/9848409326466939482023-09-22city scrapeRental unit in Rio de Janeiro · ★New · 1 bedroom · 1 bed · 1 bathapzinho em santa teresa ideal para quem busca passar uma temporada ou aproveitar o fim de semana em um dos bairros mais encantadores da regio central do rioo ambiente aconchegante bem iluminado e equipado com o necessrio para uma estadia confortvel prximo lapa mercearia bares restaurantes e outros pontos de interesse localambiente sem discriminao pessoas lgbtqia e progressistas de todos os lugares do mundo so mais que bemvindas qualquer dvida s mandar uma mensagemno_infohttps://a0.muscache.com/pictures/miso/Hosting-984840932646693948/original/c9da5996-5644-4e59-9559-ce8b05de5068.jpeg72765617https://www.airbnb.com/users/show/72765617Suzany2016-05-18Rio de Janeiro, Brazilno_infono_infono_infono_infofhttps://a0.muscache.com/im/pictures/user/User-72765617/original/7193f243-6433-41c7-b7fe-9c847c88a23d.png?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/User-72765617/original/7193f243-6433-41c7-b7fe-9c847c88a23d.png?aki_policy=profile_x_mediumCentro1.02.0['email', 'phone']tfno_infoSanta Teresa-22.913460-43.192810Entire rental unitEntire home/apt41 bath1.01.0["Essentials", "Dedicated workspace", "Hangers", "Smoking allowed", "Washer", "Hot water", "Kitchen", "Luggage dropoff allowed", "Wifi"]127.01365113653651.0365.0t3060902702023-09-22000no_infono_infono_infono_infono_infono_infono_infono_infono_infot1100no_info
31956984914648490130890https://www.airbnb.com/rooms/9849146484901308902023-09-22city scrapeHome in Rio de Janeiro · ★New · 1 bedroom · 2 beds · 1 bathnossa casa uma aconchegante residncia localizada no alto do vidigal prximo as vistas deslumbrantes para o mar e acesso fcil s praias da regio prximo a trilha dois irmos quartos confortveis uma decorao acolhedora e uma atmosfera descontrada e familiar perfeita para relaxar aps um dia de explorao na cidade alm disso estamos disponveis para dar dicas sobre os melhores lugares para visitar restaurantes e atividades culturais durante a sua estadia no vidigal e no rjno_infohttps://a0.muscache.com/pictures/miso/Hosting-984914648490130890/original/5c355dbd-6452-4dc7-964c-1a966d628234.jpeg72723753https://www.airbnb.com/users/show/72723753Isabela2016-05-18Rio de Janeiro, Brazilno_infono_infono_infono_infofhttps://a0.muscache.com/im/pictures/user/8ce01287-0b0e-4949-bb86-146ad21f5015.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/8ce01287-0b0e-4949-bb86-146ad21f5015.jpg?aki_policy=profile_x_mediumVidigal1.02.0['phone']ttno_infoVidigal-22.995969-43.244805Private room in homePrivate room21 bath1.02.0["Dedicated workspace", "BBQ grill", "Coffee", "Cooking basics", "Baking sheet", "Wine glasses", "Bed linens", "Washer", "Wifi", "Drying rack for clothing", "Dishes and silverware", "Barbecue utensils", "Cleaning products", "Hair dryer", "TV", "Iron", "Outdoor dining area", "Dining table", "Toaster", "Blender", "Essentials", "Gas stove", "Hot water", "Extra pillows and blankets", "Kitchen", "Stainless steel oven", "Air conditioning"]119.01365113653651.0365.0t2959892692023-09-22000no_infono_infono_infono_infono_infono_infono_infono_infono_infof1010no_info
31957984954786867790689https://www.airbnb.com/rooms/9849547868677906892023-09-23city scrapeRental unit in Rio de Janeiro · ★New · 1 bedroom · 1 bed · 1 bathfica quase p na areia no baixo copa aqui tem tudo perto metro ponto de txi mercado embaixo e outros bem prximos uma variedade de farmcia e hospitais alem de uma rede excelente de bares e restaurante um povo acolhedor e animado tranquilo na sala tem um sof cama com uma cama auxiliarno_infohttps://a0.muscache.com/pictures/f1f0cd35-eacb-4981-8ab8-3bd5c453f926.jpg83378610https://www.airbnb.com/users/show/83378610Rodeslir Nunes De2016-07-12Rio de Janeiro, BrazilDoida e Feliz. kkkkwithin a few hours100%60%fhttps://a0.muscache.com/im/pictures/user/e718f398-6db5-4de7-9b17-809f0329604d.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/e718f398-6db5-4de7-9b17-809f0329604d.jpg?aki_policy=profile_x_mediumCopacabana4.04.0['email', 'phone']ttno_infoCopacabana-22.975583-43.189244Entire rental unitEntire home/apt41 bath1.01.0["Washer", "TV", "Wifi", "Kitchen"]700.01365113653651.0365.0t2953832522023-09-23000no_infono_infono_infono_infono_infono_infono_infono_infono_infof2200no_info
31958984978374428458039https://www.airbnb.com/rooms/9849783744284580392023-09-23city scrapeRental unit in Rio de Janeiro · ★New · 1 bedroom · 1 bed · 2 bathsrelaxe neste espao calmo e cheio de estilo apartamento no bairro mais acolhedor e jovem do rio de janeiro rua sem sada com segurana lindamente arborizada silncio absoluto considerada a rua mais tranquila do leme esse bairro pequeno mas oferece tudo que um viajante pode precisar mercados padaria farmcia restaurantes hortifruti ponto de nibus lojinhas e a praia mais gostosa da zona sul o apt fica a m da praia quartinho com banheiro ao lado dois colches e at redeApartamento no bairro mais acolhedor e jovem do Río de Janeiro! Rua sem saída com segurança. Lindamente arborizada, silêncio absoluto, considerada a rua mais tranquila do Leme! Esse bairro é pequeno mas oferece tudo que um viajante pode precisar! Mercados, padaria, farmácia, restaurantes, hortifruti, ponto de ônibus, lojinhas e a praia mais gostosa da zona sul! O apt fica a 400 m da praia .https://a0.muscache.com/pictures/miso/Hosting-984978374428458039/original/9d114138-a6d8-48e0-bfa9-68f5bbed4ee2.jpeg102525788https://www.airbnb.com/users/show/102525788Letícia2016-11-04Rio de Janeiro, Brazilno_infowithin a few hours90%33%fhttps://a0.muscache.com/im/pictures/user/User-102525788/original/1ecd3531-9d52-4c46-8624-f062ce09f37f.jpeg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/User-102525788/original/1ecd3531-9d52-4c46-8624-f062ce09f37f.jpeg?aki_policy=profile_x_mediumno_info2.03.0['email', 'phone']ttRio de Janeiro, BrazilLeme-22.962480-43.173710Private room in rental unitPrivate room22 baths1.01.0["Free parking on premises", "Dedicated workspace", "Smoking allowed", "Washer", "TV", "Fire extinguisher", "Kitchen", "Wifi", "Smoke alarm"]102.01365113653651.0365.0t2144732412023-09-23000no_infono_infono_infono_infono_infono_infono_infono_infono_infof2020no_info
31959985064291460771751https://www.airbnb.com/rooms/9850642914607717512023-09-22city scrapeRental unit in Rio de Janeiro · ★New · 1 bedroom · 1 bed · 1 bathrelaxe com toda a famlia nesta acomodao tranquilano_infohttps://a0.muscache.com/pictures/hosting/Hosting-985064291460771751/original/89627e99-bb16-4e73-b2cb-e0b4df261d43.jpeg401842074https://www.airbnb.com/users/show/401842074Nayana2021-05-16Rio de Janeiro, BrazilOlá! Me chamo Nayana, sou casada e mãe de Pet! Gosto muito de passear, viajar e dar uma bela caminhada na praia curtindo a orla! Sou uma pessoa receptiva e comunicativa! Espero que gostem e aprovem a estadia nos apartamentos disponíveis! Sua avaliação será muito importante pra mim! Obrigada pela sua visita! Voltem sempre que puder! Beijinhoswithin an hour100%63%fhttps://a0.muscache.com/im/pictures/user/4ab135fa-455c-4a5d-ac22-23a484d06855.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/4ab135fa-455c-4a5d-ac22-23a484d06855.jpg?aki_policy=profile_x_mediumBarra da Tijuca26.027.0['phone']ttno_infoBarra da Tijuca-23.009810-43.344824Entire rental unitEntire home/apt21 bath1.01.0["Beach access", "Cooking basics", "Pool", "Pool table", "Free parking on premises", "Sea view", "Washer", "Pool view", "Wifi", "Ocean view", "Dishes and silverware", "Courtyard view", "Exercise equipment", "Outdoor shower", "TV", "Lake access", "Beach view", "City skyline view", "Essentials", "Smoking allowed", "Kitchen", "Air conditioning"]400.02302530302.130.0t2757871292023-09-22000no_infono_infono_infono_infono_infono_infono_infono_infono_infof242400no_info
31960985340991466900379https://www.airbnb.com/rooms/9853409914669003792023-09-22city scrapeRental unit in Rio de Janeiro · ★New · 2 bedrooms · 2 beds · 2 bathshelloseason apartment exclusive electronic lock up to people cable tv wifi air conditioning full close to the beach it is on rua santa clara copacabana close to transport shops and services the region is very charming well frequented and with many shops and interesting places to eat and visitevery days we change bed and bath linen just ask ok count on us to plan your trip tips tickets and laundrythe spaceyour accommodation has double bed sofa cable tv wifi air conditioner household items and hot waterduring your stay we can change bed and bath sheets at each days please let us know prior to day as you want itat the neighboorhood you are close to the mall local buzz beautiful views ipanema and transport options shops and banks close to the beach residential building cozy ideal for families who come to relax and even workguest accessbrCopacabana is a 24/7 neighborhood and very charming!<br /><br />You will be a few steps to one of the most famous beaches in the world, local shops, pharmacies, markets, street markets, not to mention the main transport in the city: taxi, uber, bus and subway. From the airports it is about 20 minutes from the SDU and 50 minutes from the GIG.<br /><br />In the surroundings there are restaurants from cheap to charming, as well as markets, pharmacies, shops and everything you might need in an ordinary neighborhood, practically 24 hours a day.<br /><br />Count on us for anything you need tickets or questions about the city!https://a0.muscache.com/pictures/prohost-api/Hosting-985340991466900379/original/7ec9473b-f400-45e2-9f62-586212e80879.jpeg30165706https://www.airbnb.com/users/show/30165706Yes Temporada2015-03-28Rio de Janeiro, BrazilOlá! Bem vindos!\n\nSomos a Yes Temporada, Anfitriões Profissionais desde 2008!\n\nImagina que você pode se hospedar com Qualidade de Hotel e Preço de Temporada? Privacidade, Higiene e Qualidade associadas ao Preço. É o melhor dos mundos!\n\nTodos os nossos apartamentos são destinados a hospedar viajantes e gerenciados de Segunda a Sábado por uma equipe que trabalha com muito carinho para garantir a melhor experiência aos hóspedes!\n\nPensamos que os espaços devem ser exclusivos, confortáveis e equipados, com tudo a seu serviço! Queremos que cada hóspede possa contar conosco para tudo que precisar na cidade e se sinta em casa! Nada como contar suporte ou ajuda quando se está fora de casa, né?\n\nSe você é uma empresa e precisa de nota fiscal ou fatura, podemos emitir!\n\nSomos um orgulhoso membro do BNI, no Rio de Janeiro. Se você é do BNI também, nos avise e teremos um prazer enorme me lhe presentear!\n \nEntre e experimente o Rio de perto!\n\n***\n\nHello! Welcome!\n\nWe are Yes Temporada, Professional Hosts since 2008!\n\nImagine you can try Hotel Quality in a Holiday Apartment? Privacy, Cleaning and Quality associated with Price. It's the best of all worlds!\n\nAll of our apartments are designed to host travelers and managed from Monday to Saturday by a team that works with great care to ensure the best experience for guests!\n\nWe believe that spaces shall be exclusive, comfortable and equipped, with everything at your service! We want each guest to count on us for everything at the city and feel home! Nothing like counting support or help when you're away from home, right?\n\nIf you´re a company and need a ticket or invoice, please let us know, we can print it for you, ok?\n\nWe are a Proud BNI member at Rio de Janeiro. If you are a BNI Member also, let us know and we will grant you with a gift!\n \nGet in and experience Rio up close!\n\n***\n\n¡Hola! ¡ Bienvenido!\n\n¡Somos Yes Temporada, Anfitriónes Profissionales desde 2008!\n\n¿Imagina que puede alojarse en un apartamento de vacaciones, pero tiene la certeza de un estándar de hotel? Privacidad, Aseo y Calidad asociadas con el precio. ¡Es el mejor de todos los mundos!\n\nTodos nuestros apartamentos son propiedades diseñadas para recibir viajeros y administradas de lunes a sábado por un equipo que trabaja con gran cuidado para garantizar la mejor experiencia para los huéspedes.\n\n¡Creemos que los espacios deben ser exclusivos, cómodos y equipados, con todo a su servicio! ¡Queremos que cada huésped cuente con nosotros para todo lo que necesita en la ciudad y sentirse como en casa! Nada como contar apoyo o ayuda cuando estás lejos de casa, ¿verdad?\n\nSi eres una empresa y necesita factura, hablanos y teremos placer en emitir!\n\nSomos un miembro orgulloso del BNI en Río de Janeiro. Si usted también es un miembro BNI, háganos saber y tenemos um regallo para usted!\n \n¡Entra y experimenta Río de cerca!\nwithin an hour99%100%fhttps://a0.muscache.com/im/pictures/user/da4143cd-6939-4e65-b412-4f7990518552.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/da4143cd-6939-4e65-b412-4f7990518552.jpg?aki_policy=profile_x_mediumCopacabana74.076.0['email', 'phone']ttRio de Janeiro, BrazilCopacabana-22.965580-43.192590Entire rental unitEntire home/apt42 baths2.02.0["Dedicated workspace", "BBQ grill", "Hangers", "Carbon monoxide alarm", "Beach essentials", "Paid parking off premises", "Free street parking", "Pets allowed", "Elevator", "Outlet covers", "Cooking basics", "Pool", "Free parking on premises", "Shampoo", "Room-darkening shades", "Bed linens", "Backyard", "Pocket wifi", "Microwave", "Paid parking on premises", "Pack \u2019n play/Travel crib", "Wifi", "Smoke alarm", "Dishes and silverware", "Ethernet connection", "Hair dryer", "Coffee maker", "Iron", "Fire extinguisher", "Long term stays allowed", "Toaster", "Essentials", "TV with standard cable", "Refrigerator", "Stove", "Hot water", "Extra pillows and blankets", "Oven", "Air conditioning", "Kitchen", "Luggage dropoff allowed", "Baby safety gates"]427.02902790903.290.0t2344743492023-09-22000no_infono_infono_infono_infono_infono_infono_infono_infono_infot676430no_info
31961985507696630141934https://www.airbnb.com/rooms/9855076966301419342023-09-22city scrapeRental unit in Rio de Janeiro · ★New · 1 bedroom · 1 bed · 1 batho aparthotel villa del sol fica localizado na praia do pontal e oferece piscinas sauna restaurante garagem e acomodaes completas com uma vista deslumbrante para o mar a proximidade com as praias da barra grumari e prainha fazem todo o diferencial alm da facilidade de estar perto de shoppings e vasto comrcio o apto contm quarto sala com cozinha americana banheiro varanda ar condicionado wifi e tv a cabo o aluguel pode ser mensal ou diriano_infohttps://a0.muscache.com/pictures/hosting/Hosting-985507696630141934/original/ba5b7966-2e07-4505-ab79-0326a4d0f9bf.jpeg416337225https://www.airbnb.com/users/show/416337225Claudio2021-08-01Rio de Janeiro, BrazilNossa famlia sempre gostou de acolher, conhecer gente nova, empreender e desbravar outros mundos.\n\n Depois de tantos anos entre dois continentes, ja nao era a hora de poder experimentar o que sempre fizemos juntos : receber gente em nossa casa com simplicidade e o melhor que podemos oferecer !\n\nSejam bem-vindos à nosso cantinho de amor ! \n\nClaudio, Tati e Vânia\nno_infono_info0%fhttps://a0.muscache.com/im/pictures/user/5fa2f746-c8fa-481d-bd5e-e5a7e7f30b32.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/5fa2f746-c8fa-481d-bd5e-e5a7e7f30b32.jpg?aki_policy=profile_x_mediumno_info2.03.0['email', 'phone']ttno_infoRecreio dos Bandeirantes-23.028250-43.499430Entire rental unitEntire home/apt41 bath1.01.0["Beach view", "Kitchen", "Beach access", "TV", "Carbon monoxide alarm", "Air conditioning", "Fire extinguisher", "Paid parking on premises", "Wifi", "Smoke alarm"]479.02365253653652.1365.0t2959892692023-09-22000no_infono_infono_infono_infono_infono_infono_infono_infono_infof2200no_info
31962985510361579508644https://www.airbnb.com/rooms/9855103615795086442023-09-22city scrapeRental unit in Rio de Janeiro · ★New · 2 bedrooms · 4 beds · 3 bathsapartamento amplo com uma vaga de garagem localizado em um dos melhores pontos de ipanema a apenas quatro quarteires da praia minutos a p a duas quadras da lagoa rodrigo de freitas mais um dos cartes postais da cidade a propriedade tem quartos amplos sendo um uma sute cozinha completa e funcional sala com mesa de jantar smart tv sof cama sala bem iluminada e ventilada trazendo uma sensao de conexo com o natureza wifi rpido ar condicionado nos quartos ventiladorthe spacesala ampla ambientes sof cama um living mesa de jantar pessoas uma mesa bistr pessoas e smart tv grandesute com cama de casal amplo guardaroupa roupas de cama travesseiro e tolhas quarto com duas camas de solteiro arcondicionado e ventilador de teto guardaroupa grandecozinha com conjunto de panelas talheres copos canecas e outros utenslios alm de liquidificador microondas cafeteRua residencial tranquila, a 4 quarteirões da praia (6minutos a pé). Nosso prédio fica ao lado de uma padaria maravilhosa com almoço e itens básicos, nas rua paralelas, você encontrará o coração comercial do bairro de Ipanema com tudo o que você precisa para ter uma excelente experiência no bairro: farmácias 24 horas, restaurantes, bares, shoppings, mercados, bancas de jornal, além das feiras gastronômicas tradicionais e artesanato que são organizados uma vez por semana nas praças do bairro. Destaca-se que esta é uma rua segura, familiar e rica em movimento.https://a0.muscache.com/pictures/hosting/Hosting-985510361579508644/original/c368392e-f0fd-446e-91fa-2bcfdb1c505f.jpeg501556443https://www.airbnb.com/users/show/501556443Camila2023-02-17Rio de Janeiro, BrazilSou apaixonada pela vida e pelo ser humano. Bora viver intensamente!!no_infono_infono_infofhttps://a0.muscache.com/im/pictures/user/User-501556443/original/aeb952c2-9511-4e72-9461-6122fe8672cc.jpeg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/User-501556443/original/aeb952c2-9511-4e72-9461-6122fe8672cc.jpeg?aki_policy=profile_x_mediumIpanema1.04.0['email', 'phone']ttRio de Janeiro, BrazilIpanema-22.981860-43.200388Entire rental unitEntire home/apt53 baths2.04.0["Free parking on premises", "Host greets you", "TV", "Kitchen", "Pets allowed", "Wifi", "Air conditioning"]582.02120221201202.0120.0t3060901692023-09-22000no_infono_infono_infono_infono_infono_infono_infono_infono_infot1100no_info
31963985555107088259155https://www.airbnb.com/rooms/9855551070882591552023-09-23city scrapeRental unit in Rio de Janeiro · ★New · 1 bedroom · 3 beds · 1 bathother things to noteseason rentals rio with sqm divided into bedroom living room bathroom and kitchenequipped with double bed single bed lcd tv and air conditioningadditional services such as wireless internet and cable tv are already included in the price of our rio season rentalsthis rio de janeiro vacation rental apartment in copacabana is located in a secure building with hour conciergethe location of this vacation rental apartment in rio offers complete infrastructure for touristshaving around supermarkets atms restaurants and public transportationthis apartment is a great vacation rental option for couples singles or small familiesno_infohttps://a0.muscache.com/pictures/prohost-api/Hosting-985555107088259155/original/e49e96a5-fb6c-44b5-9ec8-87fca3659b27.jpeg171256195https://www.airbnb.com/users/show/171256195Rio Rentals 0212018-02-02Rio de Janeiro, BrazilOlá, somos a Rio Rentals 021, a alguns anos trabalhamos ajudando pessoas a encontrarem lugares ideais para passarem suas férias, sempre sendo prestativo e atencioso com as necessidades dos hóspedeswithin an hour100%99%fhttps://a0.muscache.com/im/pictures/user/ab467d5a-100a-43de-97f4-40c638c085b8.jpg?aki_policy=profile_smallhttps://a0.muscache.com/im/pictures/user/ab467d5a-100a-43de-97f4-40c638c085b8.jpg?aki_policy=profile_x_mediumCopacabana5.07.0['email', 'phone']ttno_infoCopacabana-22.962774-43.175722Entire rental unitEntire home/apt41 bath1.03.0["Bed linens", "TV with standard cable", "Refrigerator", "Smoking allowed", "Microwave", "Iron", "Kitchen", "Pets allowed", "Wifi", "Elevator", "Air conditioning"]202.01901109011251.4584.9t2750801392023-09-23000no_infono_infono_infono_infono_infono_infono_infono_infono_infot4400no_info